epa- september 1996 hydrological simulation program - fortran
TRANSCRIPT
EPA- September 1996
HYDROLOGICAL SIMULATION PROGRAM - FORTRAN USER'S MANUAL FOR RELEASE 11
Brian R. BicknellJohn C. Imhoff
John L. Kittle, Jr. Anthony S. Donigian, Jr.
AQUA TERRA ConsultantsMountain View, California 94043
Robert C. Johanson
University of the Pacific Stockton, California 95204
Project Officer
Thomas O. BarnwellAssessment Branch
Environmental Research Laboratory Athens, Georgia 30613
IN COOPERATION WITH
OFFICE OF SURFACE WATERWATER RESOURCES DIVISIONU.S. GEOLOGICAL SURVEYRESTON, VIRGINIA 20192
ENVIRONMENTAL RESEARCH LABORATORY OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCYATHENS, GEORGIA 30613
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DISCLAIMER
This report has been reviewed by the Environmental Research Laboratory, U.S.Environmental Protection Agency, Athens, Georgia and approved for publication.Approval does not signify that the contents necessarily reflect the views andpolicies of the U.S. Environmental Protection Agency, nor does mention of tradenames or commercial products constitute endorsement or recommendation for use.
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FOREWORD
As environmental controls become more costly to implement and the penalties ofjudgment errors become more severe, environmental quality management requires moreefficient analytical tools based on greater knowledge of the environmentalphenomena to be managed. As part of this Laboratory's research on the occurrence,movement, transformation, impact, and control of environmental contaminants,the Assessment Branch develops management or engineering tools to help pollutioncontrol officials achieve water quality goals through watershed management.
The development and application of mathematical models to simulate the movement ofpollutants through a watershed and thus to anticipate environmental problems hasbeen the subject of intensive EPA research for a number of years. An importanttool in this modeling approach is the Hydrological Simulation Program - FORTRAN(HSPF), which uses computers to simulate hydrology and water quality in natural andman-made water systems. HSPF is designed for easy application to most watershedsusing existing meteorologic and hydrologic data. Although data requirements areextensive and running costs are significant, HSPF is thought to be the mostaccurate and appropriate management tool presently available for the continuoussimulation of hydrology and water quality in watersheds. Rosemarie C. Russo, Ph.D. Director Environmental Research Laboratory Athens, Georgia
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ABSTRACT
The Hydrological Simulation Program - FORTRAN (HSPF) is a set of computer codesthat can simulate the hydrologic, and associated water quality, processes onpervious and impervious land surfaces and in streams and well-mixed impoundments.The manual discusses the structure of the system, and presents a detaileddiscussion of the algorithms used to simulate various water quantity and qualityprocesses. It also contains all of the information necessary to develop inputfiles for applying the program, including descriptions of program options,parameter definitions, and detailed input formatting data.
The original version of this report was submitted in fulfillment of Grant No.R804971-01 by Hydrocomp, Inc., under the sponsorship of the U.S. EnvironmentalProtection Agency. That work was completed in January 1980. Extensive revisions, modifications, and corrections to the original report and theHSPF code were performed by Anderson-Nichols and Co. under Contract No. 68-03-2895,also sponsored by the U.S. EPA. That work was completed in January 1981. Versions7 and 8 of HSPF and the corresponding documents were prepared by Linsley, KraegerAssociates, Ltd. and Anderson-Nichols under Contract No. 68-01-6207, the HSPFmaintenance and user support activities directed by the U.S. EPA laboratory inAthens, GA.
The HSPF User's Manual for Versions 10 and 11 were prepared by AQUA TERRAConsultants of Mountain View, CA, incorporating code modifications, corrections,and documentation of algorithm enhancements sponsored by the U.S. GeologicalSurvey, the U.S. EPA Chesapeake Bay Program, the U.S. Army Corps of Engineers, andthe U.S. EPA Athens Environmental Reasearch Laboratory. The Version 11 manual andcode were prepared under sponsorship of the U.S. Geological Survey under ContractNo. 14-08-0001-23472. The manual is available in WordPerfect format.
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CONTENTS
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Part A Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . 1 B General Principles. . . . . . . . . . . . . . . . . . . . . . . . 8 C Standards and Conventions (not included). . . . . . . . . . . . . 24 D Visual Table of Contents (not included) . . . . . . . . . . . . . 24 E Functional Description. . . . . . . . . . . . . . . . . . . . . . 25 F Format for the Users Control Input. . . . . . . . . . . . . . . . 284 Appendices I Glossary of Terms . . . . . . . . . . . . . . . . . . . . . . . . 744 II Time Series Concepts. . . . . . . . . . . . . . . . . . . . . . . 752
Introduction
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PART A
INTRODUCTION
CONTENTS 1.0 Purpose and Scope of the HSPF Software . . . . . . . . . . . . . 22.0 Requirements for HSPF . . . . . . . . . . . . . . . . . . . . . . 43.0 Purpose and Organization of this Document . . . . . . . . . . . . 54.0 Definition of Terms . . . . . . . . . . . . . . . . . . . . . . . 65.0 Notice of User Responsibility . . . . . . . . . . . . . . . . . . 66.0 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 6
Introduction
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1.0 PURPOSE AND SCOPE OF THE HSPF SOFTWARE The use of models which simulate continuously the quantity/quality processesoccurring in the hydrological cycle is increasing rapidly. Recently there has beena proliferation in the variety of models and in the range of processes theysimulate. This has been a mixed blessing to a user. To get the benefits ofsimulation, a user must select a model from a bewildering array and then spend mucheffort amassing and manipulating the huge quantities of data which the modelrequires. If the modeler wishes to couple two or more subprocess models tosimulate a complete process, he often encounters further difficulties. Theunderlying assumptions and/or structures of the subprocess models may make themsomewhat incompatible. More frequently, the data structures are so different thatcoupling requires extensive data conversion work. One reason for these problems is that the boom in modeling work has not includedenough work on the development of good model structures. That is, very fewsoftware packages for water resource modeling are built on a systematic frameworkin which a variety of process modules can fit. With HSPF we have attempted to overcome these problems as far as possible. HSPFconsists of a set of modules arranged in a hierarchical structure, which permit thecontinuous simulation of a comprehensive range of hydrologic and water qualityprocesses. Our experience with sophisticated models indicates that much of thehuman effort is associated with data management. This fact, often overlooked bymodel builders, means that a successful comprehensive model must include a sounddata management component. Otherwise, the user may become so entangled in datamanipulation that his progress on the simulation work itself is drasticallyretarded. Consequently, the HSPF software is planned around a time seriesmanagement system operating on direct access principles. The simulation modulesdraw input from time series storage files and are capable of writing output tothem. Because these transfers require very few instructions from the user, theproblems referred to above are minimized. The system is designed so that the various simulation and utility modules can beinvoked conveniently, either individually or in tandem. A top down approachemphasizing structured design has been followed. First, the overall framework andthe Time Series Management System were designed. Then, work progressed down thestructure from the highest, most general level to the lowest, most detailed one.Every level was planned before the code was written. Uniform data structures,logic figures, and programming conventions were used throughout. Modules wereseparated according to function so that, as much as possible, they contained onlythose activities which are unique to them. Structured design has made the systemrelatively easy to extend, so that users can add their own modules with relativelylittle disruption of the existing code.
Introduction
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Now, a note on the initial contents of the system. Presently, it includes moduleswhich can handle almost all the functions which are available in the followingexisting models: (1) HSP (LIBRARY, UTILITY, LANDS, CHANNEL, QUALITY) (2) ARM (3) NPS (4) SERATRA The HSPF software is not merely a translation of the above models, but a new systemwith a framework designed to accommodate a variety of simulation modules; themodules described above are the initial contents. Many extensions have been madeto the above models in the course of restructuring them into the HSPF system. It is hoped that HSPF will become a valuable tool for water resource planners.Because it is more comprehensive than most existing systems, it should permit moreeffective planning. More specifically, the package can benefit the user in thefollowing ways: 1. The time-series-oriented direct access data system and its associated modules
can serve as a convenient means of inputting, organizing, and updating thelarge files needed for continuous simulation.
2. The unified user-oriented structure of the model makes it relatively simple
to operate. The user can select those modules and options that are needed inone run, and the system will ensure that the correct sets of code are invokedand that internal and external transfers of data are handled. This isachieved with a minimum of manual intervention. Input of control informationis simplified because a consistent system is used for this data for all themodules.
3. Because the system has been carefully planned using top-down programming
techniques, it is relatively easy to modify and extend. The use of uniformprogramming standards and conventions has assisted in this respect.
4. Since the code is written almost entirely in ANSI standard Fortran,
implementation on a wide variety of computers is possible.
Introduction
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2.0 REQUIREMENTS FOR HSPF In awarding the grant for development of HSPF, the EPA set the followingrequirements:
1. It must manage and perform deterministic simulation of a variety of aquaticprocesses which occur on and under land surfaces and in channels andreservoirs.
2. It must readily accommodate alternate or additional simulation modules. 3. It must permit easy operation of several modules in series, and thus be
capable of feeding output from any operation to subsequent operations. 4. It must be in ANSI Fortran with minor specified extensions. With the concurrence of the EPA, we expanded on these requirements: 1. It must have a totally new design. Existing modules should not merely be
translated, but should be fitted into a new framework. 2. It must be designed from the top down, using some of the new improved
programming techniques, such as Structured Design and Structured Programming. 3. Duplication of blocks of code which perform similar or identical functions
should be avoided. 4. The user's control input must have a logically consistent structure throughout
the package. 5. Uniform standards and practices must be followed throughout the design,
development and documentation of the system. 6. It must have a conveniently operated disk-based time series storage file built
on the principle of direct access. 7. The design must be geared to implementation on larger models of the current
generation of minicomputers. It must be compatible with Operating Systemswhich share memory using either the virtual memory approach or a conventionaloverlay technique.
Introduction
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3.0 PURPOSE AND ORGANIZATION OF THIS DOCUMENT This report contains all the documentation of the HSPF system. It is designed to: 1. introduce new users to the principles and concepts on which the system is
founded 2. describe the technical foundations of the algorithms in the various
application (simulation) modules 3. describe the input which the user supplies to run the system To meet these needs and, at the same time, to produce a document which isreasonably easy to use, we have divided this report into several distinct parts,each with its own organization and table of contents. Part A (this one) contains introductory material. Part B outlines the general principles on which the HSPF system is based. Thisincludes a discussion of the "world view" which our simulation modules embody. Afirm grasp of this material is necessary before the detailed material can beproperly understood. Part C Standards and Conventions (not included) Part D Visual Table of Contents (not included)
Part E documents the function of each part of the software. The organization ofthis part follows the layout of the software itself. The relationship between, andthe functions of, the various modules are described, starting at the highest mostgeneral level and proceeding down to the lowest most detailed level. The algorithmsused to simulate the quantity and quality processes which occur in the real worldare described in this part. Part F describes the User's Control Input; that is, the information which the usermust provide in order to run HSPF. Material which might obscure the structure of this document if it were included inthe body of the report appears in Appendices. These include a glossary of termsand descriptions of sample runs.
Introduction
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4.0 DEFINITION OF TERMS
In this document, terms which have a special meaning in HSPF, are enclosed inquotes the first time they occur. Usually an explanation follows immediately. Aglossary of terms can be found in Appendix I.
5.0 NOTICE OF USER RESPONSIBILITY
This product has been carefully developed. Although the work included testing ofthe software, the ultimate responsibility for its use and for ensuring correctnessof the results obtained, rests with the user. The EPA and the developers of this software make no warranty of any kind withregard to this software and associated documentation, including, but not limitedto, the implied warranties of merchantability and fitness for a particular purpose.They shall not be liable for errors or for incidental or consequential damages inconnection with the furnishing, performance or use of this material. While we intend to correct any errors which users report, we are not obliged to doso. We reserve the right to make a reasonable charge for work which is performedfor a specific user at his request. 6.0 ACKNOWLEDGMENTS The original development of HSPF was sponsored by the Environmental ResearchLaboratory in Athens, Georgia. David Duttweiler was the laboratory director andRobert Swank the head of the Technology Development and Applications Branch, whichsupervised the project during the code development period. Mr. Jim Falco was theProject Officer initially on the HSPF development work; he was succeeded by Mr. TomBarnwell who continues to oversee HSPF support activities for EPA.
Recent development of HSPF has been sponsored by the U.S. Geological Survey WaterResources Division in Reston, Virginia. Dr. Alan Lumb is the Contract Officer anddirects that effort for the USGS.
The initial HSPF and user manual development work was performed by Hydrocomp, Inc.;members of the entire project team are acknowledged in the original (Release 5.0)version of the user manual (EPA Publication No. EPA-600/9-80-015) published inApril 1980. Subsequent revisions and extensions to the HSPF code and user manualwere performed by Anderson-Nichols & Co., Inc. and AQUA TERRA Consultants. Theprimary participants in the work noted above, and their contributions, arediscussed below.
Robert Johanson was Project Manager for Hydrocomp on the initial development work,and provided consulting assistance to Anderson-Nichols & Co., Inc. and Linsley,Kraeger Associates on subsequent development and maintenance work.
John Imhoff had primary responsibility for the RCHRES water quality sections, bothduring the initial development work for Hydrocomp and subsequent modifications anddevelopment for Anderson-Nichols and AQUA TERRA.
Introduction
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Harley Davis designed, coded and documented much of the PERLND and IMPLND modulesfor Hydrocomp during the initial development effort.
Delbert Franz participated in the overall design of the system and supervised thework on the time series management system at Hydrocomp. Jack Kittle performed or directed most HSPF software development activities since1979. His focus has been on the software structure, time series improvements, andaddition of new components. He designed the MUTSIN (Multiple Timeseries SequentialInput) module, water categories in the RCHRES module, conditional Special Actions,major structural improvements to the software, and was responsible for productionof Releases 7, 8 and 9. He continues to be a principal advisor in HSPF developmentand maintenance. Tony Donigian participated in the design of the PERLND algorithms in the initialproject at Hydrocomp. Since 1980, he has been the Principal Investigator/ProjectManager, providing overall guidance and supervision, on all projects related toHSPF algorithm development, with particular focus on the improvements to the AGCHEMsections of the program.
Brian Bicknell has been involved in HSPF maintenance and software development since1981. He has developed or directed all recent algorithm enhancements and softwarecorrections. He added the WDM file interaction, the MASS-LINK and SCHEMATICblocks, and the FILES block. He directed the development and documentation ofVersions 10 and 11, including major enhancements for simulating forest nitrogen,atmospheric deposition, the DSS file interface, and sediment-nutrient interactionsand bed temperature interactions in RCHRES.
Tom Jobes has been the primary software engineer with day-to-day responsibility forHSPF development and maintenance since 1992. He implemented most of theenhancements and software corrections in Version 11, with particular responsibilityfor the DSS file interface, atmospheric deposition, forest nitrogen and plantuptake enhancements in AGCHEM, multiple WDM files, code structure improvements,conditional Special Actions, and water categories.
General Principles
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PART B
GENERAL PRINCIPLES
CONTENTS 1.0 View of the Real World . . . . . . . . . . . . . . . . . . . . . 9 1.1 General Concepts . . . . . . . . . . . . . . . . . . . . . 9 1.2 Nodes, Zones, and Elements . . . . . . . . . . . . . . . . 9 1.3 Processing Units and Networks . . . . . . . . . . . . . . . 11 2.0 Software Structure . . . . . . . . . . . . . . . . . . . . . . . 14 2.1 Concept of an Operation . . . . . . . . . . . . . . . . . . 14 2.2 Time Series Storage . . . . . . . . . . . . . . . . . . . . 16 2.3 Times Series Management for an Operation . . . . . . . . . 17 2.4 HSPF Software Hierarchy . . . . . . . . . . . . . . . . . . 17 3.0 Structure of a Job . . . . . . . . . . . . . . . . . . . . . . . 20 3.1 Elements of a Job . . . . . . . . . . . . . . . . . . . . . 20 3.2 Groups of Operations . . . . . . . . . . . . . . . . . . . 20
4.0 Conventions Used in Functional Description . . . . . . . . . . . 23 5.0 Method of Documenting Data Structures . . . . . . . . . . . . . 23 5.1 Structure of Data in Memory . . . . . . . . . . . . . . . . 23 5.2 Structure of Data on Disk Files . . . . . . . . . . . . . . 23 6.0 Method of Handling Diagnostic Messages . . . . . . . . . . . . . 24 FIGURES Number Page 1-1 Nodes, zones and elements . . . . . . . . . . . . . . . . . . . 101-2 Directed and non-directed graphs . . . . . . . . . . . . . . . . 121-3 Single- and multi-element processing units . . . . . . . . . . . 132-1 Logical structure of the internal scratch pad . . . . . . . . . 152-2 Activities involved in an operation . . . . . . . . . . . . . . 182-3 Overview of HSPF software . . . . . . . . . . . . . . . . . . . 193-1 Schematic of data flow and storage in a single run . . . . . . . 213-2 Extract from typical User's Control Input, showing how grouping of operations is specified . . . . . . . . . . . . . 22
General Principles
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1.0 VIEW OF THE REAL WORLD
1.1 General Concepts
To design a comprehensive simulation system, one must have a consistent means ofrepresenting the prototype; in our case, the real world. We view it as a set ofconstituents which move through a fixed environment and interact with each other.Water is one constituent; others are sediment, chemicals, etc. The motions andinteractions are called processes.
1.2 Nodes, Zones, and Elements
The prototype is a continuum of constituents and processes. Simulation of such asystem on a digital computer requires representation in a discrete fashion. Ingeneral, we do this by subdividing the prototype into "elements" which consist of"nodes" and "zones."
A node corresponds to a point in space. Therefore, a particular value of aspatially variable function can be associated with it, for example, channel flowrate and/or flow cross sectional area. A zone corresponds to a finite portion ofthe real world. It is usually associated with the integral of a spatially variablequantity, for example, storage in a channel reach. The zone the smallest unit intowhich we subdivide the world. The relationship between zonal and nodal values issimilar to that between the definite integral of a function and its values at thelimits of integration.
An element is a collection of nodes and/or zones. Figure 1-1 illustrates theseconcepts. We simulate the response of the land phase of the hydrological cycleusing elements called "segments." A segment is a portion of the land assumed tohave areally uniform properties. A segment of land with a pervious surface iscalled a "Pervious Land-segment" (PLS). Constituents in a PLS are represented asresident in a set of zones (Fig. 1-1a). A PLS has no nodes. As a further example,consider our formulation of channel routing. We model a channel reach as a onedimensional element consisting of a single zone situated between two nodes (Fig.1-1b). We simulate the flow rate and depth at the nodes; the zone is associatedwith storage.
The conventions of the finite element technique also fall within the scope of theseconcepts. Figure 1-1c shows a two dimensional finite element used in thesimulation of an estuary. Three nodes define the boundaries of the triangularelement. A fourth node, situated inside, may be viewed as subdividing the elementinto three zones. This last type of element is not presently used in any HSPFmodule, but is included in this discussion to show the generality provided by HSPF.The system can accommodate a wide variety of simulation modules.
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Figure 1-1 Nodes, zones, and elements
General Principles
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There are no fixed rules governing the grouping of zones and nodes to formelements. The model builder must decide what grouping is reasonable andmeaningful, based on his view of the real world processes being simulated. In theforegoing material we presented some elements used in HSP and other systems. Ingeneral, it is convenient to define elements so that a large portion of the realworld can be represented by a collection of conceptually identical elements. Inthis way, a single parameter structure can be defined which applies to everyelement in the group. Thus, each element is a variation on the basic theme. Itis then meaningful to speak of an "element type." For example, elements of type"PLS" all embody the same arrangement of nodes and are represented by sets ofparameters with identical structure. Variations between segments are representedonly by variations in the values of parameters. The same applies to any otherelement, such as a Reach, layered lake or a triangular finite element.
As illustrated in the above discussion, nodes are often used to define theboundaries of zones and elements. A zone, characterized by storage, receivesinflows and disperses outflows; these are called "fluxes." Note that if the nodalvalues of a field variable are known, it is often possible to compute the zonalvalues (storages). The reverse process does not work.
1.3 Processing Units and Networks
To simulate a prototype we must handle the processes occurring within the elementsand the transfer of information and constituents between them. The simulation oflarge prototypes is made convenient by designing a single "application module" fora given type of element or element group, and applying it repetitively to allsimilar members in the system. For example, we may use the RCHRES module tosimulate all the reaches in a watershed using storage routing. This approach ismost efficient computationally if one element or group of elements, called a"processing unit" (PU), is simulated for an extended period of time beforeswitching to the next one. To permit this, we must be able to define a processingsequence such that all information required by any PU comes from sources externalto the system or from PU's already simulated. This can only happen if the PU's andtheir connecting fluxes form one or more networks which are "directed graphs." Ina directed graph there are no bi-directional paths and no cycles. Figure 1-2 showssome directed and non-directed graphs.
The requirement that PU's form directed graphs provides the rule for groupingelements into PU's. Any elements interacting with each other via loops or bi-directional fluxes must be grouped into a single PU because none of them can besimulated apart from the others.
Thus, we can have both single element and multi-element PU's. A PLS is an exampleof the former and a channel network simulated using the full equations of flowexemplifies the latter (Fig. 1-3). A multi-element PU is also known as a "feedbackregion." The collection of PU's which are simulated in a given run is called a"network."
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Figure 1-2 Directed and Non-directed graphs
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Figure 1-3 Single- and multi-element processing units
General Principles
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The processes which occur within a PU are represented mathematically in an"application model." The corresponding computer code is called an "applicationmodule" or "simulation module."
2.0 SOFTWARE STRUCTURE
2.1 Concept of an Operation
A great variety of activities are performed by HSPF; for example, input a timeseries to the WDM file, find the cross correlation coefficient for two time series,or simulate the processes in a land segment. They all incorporate two or more ofthe following functions: get a set of time series, operate on the set of inputtime series to produce other time series, and output the resulting time series.This applies both to application modules (already discussed) and to utilitymodules, which perform operations ancillary or incidental to simulation. Thus, asimulation run may be viewed as a set of operations performed in sequence. Alloperations have the following structure:
SUPERVISE OPERATIONS (subroutine OSUPER) T +))))))))))))))))3))))))))))))))))), R R R GET TIME OPERATE PUT TIME SERIES (utility SERIES (subroutine group or (subroutine group TSGET) application TSPUT) module)
The OPERATE function is the central activity in the operation. This work is doneby an "operating module" (OM) and its subordinate subprograms. They operate fora specified time on a given set of input time series and produce a specified setof output time series, under control of the "operations supervisor" (OSUPER). Allof the pieces of time series involved in this internal operation have the sameinterval and duration. They are therefore viewed as written on an "internalscratch pad" (INPAD), resident in the memory of the computer (Fig. 2-1). Theoperating module receives the scratch pad with some rows filled with input and,after its work is done, returns control to the supervisor with another set of rowsfilled with output. The operating module may overwrite an input row with its ownoutput. The computing module being executed, together with the options beinginvoked, will determine the number of rows required in the INPAD. For example,simulation of the hydraulic behavior of a stream requires relatively few timeseries (eg. inflow, depth and outflow) but the inclusion of water qualitysimulation adds many more time series to the list. Now, the total quantity ofmemory space available for storage of time series is also fixed (specified in aCOMMON block) by the options in effect; this is the size (area) of the INPAD.Since both the size (N*M) and number of rows (M) in the INPAD are known, the"width" (no. of intervals,N) can be found. The corresponding physical time iscalled the "internal scratch pad span (INSPAN)."
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Figure 2-1 Logical structure of the internal scratch pad
General Principles
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The "get time series" function prepares the input time series. This work is doneby a subroutine group called TSGET. It obtains the correct piece of a time seriesfrom the appropriate file, aggregates or disaggregates it to the correct timeinterval, multiplies the values by a user specified constant (if required), andplaces the data in the required row of the internal scratch pad. Subroutine groupTSPUT performs the reverse set of operations. TSGET and TSPUT are sometimesbypassed if a required time series is already in the INPAD when the operation isstarted, or if the output is being passed to the next operation via the internalscratch pad.
2.2 Time Series Storage
The time series used and produced by an operation can reside in four types ofstorage. 1. The Watershed Data Management (WDM) File
The WDM file has replaced the TSS as the principal library for storage of timeseries. As far as the computer's operating system is concerned, it consistsof a single large direct access file. This space is subdivided into many datasets containing individual time series. Each is logically self-contained butmay be physically scattered through the file. A directory keeps track of datasets and their attributes. Before time series are written to the WDM file,the file and its directory must be created using the interactive programANNIE, which is documented separately.
2. The Hydrologic Engineering Center Data Storage System (DSS)
The DSS is the primary hydrologic data storage system of the U.S. ArmyEngineers Hydrologic Engineering Center (HEC). It is similar in design andfunction to the WDM file. A DSS file consists of a single, large, direct-access file containing many individual data sets that are identified by uniqueidentifiers called "pathnames". A pathname is a string of characters from 5to 80 characters long, and similar in construction to a file pathname oncomputer disk. Creation and maintenance of DSS files is typically performedby a utility program such as DSSUTL, which is documented separately.
3. Sequential Files
These are ASCII, formatted disk files with a constant logical record length.Time series received from agencies such as the National Weather Service aretypically stored in sequential files.
4. Internal Scratch Pad (INPAD)
If two or more operations performed in sequence use the same internal timestep, time series may be passed between them via the INPAD. Successiveoperations may simply pick up the data written by the previous ones, withoutany external (disk) transfer taking place. This is typically done when timeseries representing the flow of water (and constituents) are routed from onestream reach to the one next downstream.
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2.3 Time Series Management For An Operation
Any operation involves a subset of the activities shown in Fig. 2-2. The operatingmodule expects a certain set of time series in the INPAD. The operationssupervisor, acting under user control, ensures that the appropriate input timeseries are loaded from whichever source has been selected, and informs thecomputing module of the rows in the INPAD where it will find its input. Similararrangements hold for output of time series.
2.4 HSPF Software Hierarchy
The hierarchy of functions in HSPF is shown in Fig. 2-3. Some explanatory notesfollow. The "Run Interpreter" is the group of subprograms which reads and interprets the"Users Control Input." It sets up internal information instructing the systemregarding the sequence of operations to be performed. It stores the initialconditions and the parameters for each operation in the appropriate file on diskand creates an instruction file which will ensure that time series are correctlypassed between operations, where necessary.
The "Operations Supervisor" is a subroutine which acts on information provided bythe Run Interpreter, invoking the appropriate "application" or "utility" modules.It provides them with the correct values for parameters and state variables byreading the files created by the Run Interpreter.
Operating modules are either "application modules" or "utility modules." Theyperform the operations which make up a run. Each time one of those modules iscalled, an operation is performed for a period corresponding to the span of theinternal scratch pad (INSPAN). The Operations Supervisor ensures that the correctmodule is invoked.
"Service subprograms" perform tasks such as reading from and writing to time seriesstorage areas, adding T minutes to a given date and time, to get a new date andtime, etc.
The "Time Series Management System" (TSMS) consists of all the modules which areonly concerned with manipulation of time series or the files used to store timeseries. It includes the WDM management functions, and TSGET and TSPUT.
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Figure 2-2 Activities involved in an operation
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Figure 2-3 Overview of HSPF software
General Principles
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3.0 STRUCTURE OF A JOB
3.1 Elements of a Job A "JOB" is the work performed by HSPF in response to a complete set of UsersControl Input. It consists of one or more "RUNs". A RUN is a set of operationswhich can be performed serially, and which all cover the same period of time(span). The operations are performed in a sequence specified in the Users ControlInput. To avoid having to store large quantities of intermediate data on disk,operations may be collected in a group in which they share a common INPAD (INGRP).
3.2 Groups Of Operations
In most runs, time series have to be passed between operations. As described inSection 2.2, each operation can communicate with four different time series storageareas: the WDM file, DSS file, the INPAD, and sequential files. This isillustrated in Fig. 3-1.
Potentially, any time series required by or output by any operation can be storedin the WDM file, DSS, or a sequential file. The user simply specifies the exactorigin or destination for the time series, and the HSPF system moves the databetween that device and the appropriate row of the INPAD. This system can also beused to transfer data between operations. However, it does require that alltransferred data be written to the WDM file, DSS, or a sequential file. This maybe very cumbersome and/or inefficient and it is better to transfer data via theINPAD, where possible.
To transfer data via the INPAD, operations must share the same pad. This means thatall time series placed in the pad have the same time interval and span. Thisrequirement provides a logical basis for grouping operations; those sharing acommon INPAD are called an INGRP (Fig. 3-1). The user specifies the presence ofgroups in his "Users Control Input (UCI)." A typical sequence of input is shown inFig. 3-2.
The user also indicates (directly or indirectly) in the control input the sourceand disposition of all time series required by or output by an operation. If theuser indicates that a time series must be passed to another operation then thesystem assumes that the transfer will be made via the scratch pad. If they are notin the same INGRP there is an error. Without a common INPAD, the data must go viathe WDM file or DSS. The structure of the Users Control Input is documented inPart F.
General Principles
21
Figure 3-1 Schematic of data flow and storage for a single run
General Principles
22
The sequence of events in a run is as follows (refer to Fig.3-1). a. Operation 1 is performed until its output rows in the INPAD are filled.
b. Data are transferred from those rows to other time series storage areas, asrequired. If any of these data are not required by other operations in INGRP1,their INPAD rows are available for reuse by other operations in INGRP1.
c. Steps (a) and (b) are repeated for each operation in INGRP1.
d. Steps (a), (b), and (c) are repeated, if necessary, until the run span iscomplete.
e. The INPAD is reconfigured and work on operations 5 through 11 proceeds as insteps (a-d) above. The step repeats until all INGRP's have been handled. Therun is now complete.
Note that reconfiguration of a scratch pad implies that its contents will beoverwritten.
OPN SEQUENCE INGRP INDELT = 00:30 COPY 1 PERLND 1 END INGRP PERLND 2 INDELT = 00:30 PERLND 3 INDELT = 00:20 INGRP INDELT = 00:30 COPY 2 RCHRES 1 RCHRES 3 RCHRES 5 RCHRES 20 RCHRES 22 RCHRES 23 RCHRES 7 RCHRES 8 RCHRES 50 RCHRES 100 RCHRES 200 END INGRP INGRP INDELT = 00:10 DURANL 1 PLTGEN 1 END INGRP END OPN SEQUENCE Fig. 3-2 Extract from typical Users Control Input, showing how grouping of operations is specified
General Principles
23
4.0 CONVENTIONS USED IN FUNCTIONAL DESCRIPTION The primary purpose of the Functional Description (Part E) is: 1. to describe the functions performed by the various subprograms 2. to explain the technical algorithms and equations which the code implements. Subprograms are described in numerical order in the text. This system provides alogical progression for the descriptions. General comments regarding a group ofsubprograms can be made when the "top" subprogram is described, while detailsspecific to an individual subordinate subprogram can be deferred until that partis described. For example, a general description of the PERLND module (Section4.2(1)) is followed by more detailed descriptions of its twelve sections, ATEMP(Section 4.2(1).1) through TRACER (Section 4.2(1).12).
5.0 METHOD OF DOCUMENTING DATA STRUCTURES
5.1 Structure of Data in Memory
The way in which we arrange the variables used in our programs is important. Westructure them, as far as possible, using techniques like those used in StructuredProgram Design. We try to group data items that logically belong together. Most of the variables in an Operating Module are contained in the Operation StatusVector (OSV). The OSVs for the application modules are shown in the Programmer'sSupplement (Johanson, et al. 1979). The format used to document a data structureis similar to that used to declare a "structure" in PL/1. We do this because thetechnique is logical and convenient, not because of language considerations.
5.2 Structure of Data on Disk Files
The HSPF system makes use of two different types of disk-based data files:
1. Watershed Data Management (WDM) file and HEC Data Storage System (DSS) filescontain time series data input and output.
2. The message/information file (HSPFMSG.WDM), is a read-only, binary file thatcontains information used by the program, such as keyword names, inputformats, parameter defaults and limits, and error/warning messages.
General Principles
24
6.0 METHOD OF HANDLING DIAGNOSTIC MESSAGES HSPF makes use of two kinds of diagnostic message; error messages and warnings,which are printed to the Run Interpreter Output file during both the RunInterpretation and simulation phases of a run. These messages are all stored onthe "message/information" file: HSPFMSG.WDM. This system for storing the messageshas at least two advantages: 1. Because the messages are not embedded in the Fortran, they do not normally
occupy any memory. This reduces the length of the executable code.
2. The files are easier to maintain than if the messages were embedded in thecode. A user can obtain a listing of the contents by "exporting" data setsfrom the message file using the ANNIE program's Archive function.
Each message has been given a "maximum count". If the count for a message reachesthis value, HSPF informs the user of the fact. Then:
1. If it is an error message, HSPF quits. 2. If it is a warning, HSPF continues but suppresses any future printing of this
message. In addition to the above features, the Run Interpreter has been designed to: 1. Stop if 20 errors of any kind have been detected. This gives the user a fair
number of messages to work on, but avoids producing huge quantities of errormessages, many of which may be spurious (say, if the code could not recoverfrom early error conditions).
2. Stop at the end of its work if any errors have been detected by it. Thus,
HSPF will not enter any costly time loop if the Run Interpreter has found anyerrors in the User's Control Input.
_______________________________________________________________________________
PART C
STANDARDS AND CONVENTIONS
This section has been omitted._______________________________________________________________________________
PART D
VISUAL TABLE OF CONTENTS
This section has been omitted._______________________________________________________________________________
Functional Description
25
PART E
FUNCTIONAL DESCRIPTION
CONTENTS
General Comments . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.0 MAIN Program . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.0 Manage the Time Series Store (omitted) . . . . . . . . . . . . . 30
3.0 Interpret a RUN Data Set in the User's Control Input (Module INTERP) . . . . . . . . . . . . . . . . . . . . . . . . 30
4.0 Supervise and Perform Operations (Module OSUPER) . . . . . . . . 32 4.03 Perform Special Actions (Subroutine SPECL). . . . . . . . . 33 4.1 Get Required Input Time Series (Module TSGET) . . . . . . . 34 4.2 Perform an Operation . . . . . . . . . . . . . . . . . . . 34 4.2(1) Simulate a Pervious Land Segment (Module PERLND). . 37 4.2(1).1 Correct Air Temperature for Elevation Difference (Section ATEMP) . . . . . . . 39 4.2(1).2 Simulate Accumulation and Melting of Snow and Ice (Section SNOW) . . . . . . . 40 4.2(1).3 Simulate Water Budget for a Pervious Land Segment (Section PWATER) . . . . . . 54 4.2(1).4 Simulate Production and Removal of Sediment . . . . . . . . . . . . . . . . 75 4.2(1).5 Estimate Soil Temperatures (Section PSTEMP) . . . . . . . . . . . . 81 4.2(1).6 Estimate Water Temperature and Dissolved Gas Concentrations (Section PWTGAS) . . . 83 4.2(1).7 Simulate Quality Constituents Using Simple Relationships with Sediment and Water Yield (Section PQUAL) . . . . . . . 84 4.2(1).8 Estimate Moisture Content of Soil Layers and Fractional Fluxes (Section MSTLAY) . 92 4.2(1).9 Simulate Pesticide Behavior in Detail (Section PEST) . . . . . . . . . . . . . 95 4.2(1).10 Simulate Nitrogen Behavior in Detail (Section NITR) . . . . . . . . . . . . . 103 4.2(1).11 Simulate Phosphorous Behavior in Detail (Section PHOS) . . . . . . . . . . . . . 110 4.2(1).12 Simulate Movement of a Tracer (Section TRACER) . . . . . . . . . . . . 113
Functional Description
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4.2(2) Simulate an Impervious Land Segment (Module IMPLND) 114 4.2(2).3 Simulate the Water Budget for an Imper- vious Land Segment (Section IWATER) . . . 114 4.2(2).4 Simulate Accumulation and Removal of Solids (Section SOLIDS) . . . . . . . . . 119 4.2(2).5 Estimate Water Temperature and Dissolved Gas Concentrations (Section IWTGAS) . . . 123 4.2(2).6 Simulate Washoff of Quality Constituents Using Simple Relationships with Solids and Water Yield (Section IQUAL) . . . . . 124 4.2(3) Simulate a Free-flowing Reach or Mixed Reservoir (Module RCHRES) . . . . . . . . . . . . . . . . . . 128 4.2(3).1 Simulate Hydraulic Behavior (Section HYDR) 132 4.2(3).2 Prepare to Simulate Advection of Fully Entrained Constituents (Section ADCALC) . 153 4.2(3).3 Simulate Conservative Constituents (Section CONS) . . . . . . . . . . . . . 156 4.2(3).4 Simulate Heat Exchange and Water Temperature (Section HTRCH) . . . . . . . 161 4.2(3).5 Simulate Behavior of Inorganic Sediment (Section SEDTRN). . . . . . . . . . . . . 169 4.2(3).6 Simulate the Behavior of a Generalized Quality Constituent (Section GQUAL) . . . 185 4.2(3).7 Simulate Constituents Involved in Biochemical Transformations (Section RQUAL) . . . . . . . . . . . . . 207 4.2(3).7.1 Simulate Primary DO and BOD Balances (Subroutine Group OXRX) . . . 209 4.2(3).7.2 Simulate Primary Inorganic Nitrogen and Phosphorous Balances (Subroutine Group NUTRX) . . . . . . . 217 4.2(3).7.3 Simulate Plankton Populations and Associated Reactions (Subroutine Group PLANK) . . . . . . . 230 4.2(3).7.4 Simulate Ph, Carbon Dioxide, Total Inorganic Carbon, and Alkalinity (Subroutine Group PHCARB) . . . . . . 254 4.2(11) Copy Time Series (Utility Module COPY) . . . . . . 260 4.2(12) Prepare Time Series for Display on a Plotter (Module PLTGEN) . . . . . . . . . . . . . . . . . . 261 4.2(13) Display Time Series in a Convenient Tabular Format (Utility Module DISPLY). . . . . . . . . . . 263 4.2(14) Perform Duration Analysis on a Time Series (Utility Module DURANL) . . . . . . . . . . . . . . 268 4.2(15) Generate a Time Series from One or Two Other Time Series (Utility Module GENER). . . . . . . . . 276 4.2(16) Multiple Sequential Input of Time Series from a HSPF Stand-Alone Plotter File (Utility Module MUTSIN). . . . . . . . . . . . . . . . . . . 278 4.3 Module TSPUT . . . . . . . . . . . . . . . . . . . . . . . 279
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
Functional Description
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FIGURES Number Page
3.0-1 Functions and data transfers involved in the operations portion of HSPF. . . . . . . . . . . . . . . . . . . . . 314.2(1)-1 Structure chart for PERLND Module. . . . . . . . . . . . 384.2(1).2-1 Snow accumulation and melt processes . . . . . . . . . . 414.2(1).2-2 Flow diagram of water movement, storages and phase changes modeled in the SNOW section of the PERLND and IMPLND Application Modules . . . . . . . . . . . . . . . 434.2(1).3-1 Hydrologic cycle . . . . . . . . . . . . . . . . . . . . 554.2(1).3-2 Flow diagram of water movement and storages modeled in the PWATER section of the PERLND Application Module. . . 574.2(1).3-3 Determination of infiltration and interflow inflow . . . 614.2(1).3-4 Fraction of the potential direct runoff retained by the upper zone (FRAC) as a function of the upper zone soil moisture ratio (UZRAT) . . . . . . . . . . . . . . . . . 654.2(1).3-5 Fraction of infiltration plus percolation entering lower zone storage . . . . . . . . . . . . . . . . . . . 704.2(1).3-6 Potential and actual evapotranspiration from the lower zone . . . . . . . . . . . . . . . . . . . . . . . . . . 744.2(1).4-1 Erosion processes. . . . . . . . . . . . . . . . . . . . 764.2(1).4-2 Flow diagram for SEDMNT section of PERLND Application Module . . . . . . . . . . . . . . . . . . . . . . . . . 774.2(1).7-1 Flow diagram for PQUAL section of PERLND Application Module . . . . . . . . . . . . . . . . . . . . . . . . . 854.2(1).8-1 Flow diagram of the transport of moisture and solutes, as estimated in the MSTLAY section of the PERLND Application Module . . . . . . . . . . . . . . . . . . . 934.2(1).9-1 Flow diagram showing modeled movement of chemicals in solution . . . . . . . . . . . . . . . . . . . . . . . . 974.2(1).9-2 Freundlich isotherm calculations . . . . . . . . . . . . 1014.2(1).10-1 Flow diagram for nitrogen reactions. . . . . . . . . . . 1044.2(1).11-1 Flow diagram for phosphorus reactions. . . . . . . . . . 1124.2(2)-1 Impervious land segment processes. . . . . . . . . . . . 1154.2(2)-2 Structure chart for IMPLND Module. . . . . . . . . . . . 1164.2(2).3-1 Hydrologic processes . . . . . . . . . . . . . . . . . . 1174.2(2).4-1 Flow diagram of the SOLIDS section of the IMPLND Application Module . . . . . . . . . . . . . . . . . . . 1204.2(2).6-1 Flow diagram for IQUAL section of IMPLND Application Module . . . . . . . . . . . . . . . . . . . . . . . . . 1254.2(3)-1 Flow of materials through a RCHRES . . . . . . . . . . . 1294.2(3)-2 Structure chart for RCHRES Module. . . . . . . . . . . . 1304.2(3).1-1 Flow diagram for the HYDR Section of the RCHRES Application Module . . . . . . . . . . . . . . . . . . . 1334.2(3).1-2 Graphical representation of the equations used to compute outflow rates and volume . . . . . . . . . . . . . . . . 1364.2(3).1-3 Typical RCHRES configurations and the method used to represent geometric and hydraulic properties . . . . . . 1374.2(3).1-4 Graphical representation of the work performed by subroutine ROUTE . . . . . . . . . . . . . . . . . . . . 140
Functional Description
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FIGURES (continued) Number Page
4.2(3).1-5 Graphical representation of the work performed by subroutine NOROUT. . . . . . . . . . . . . . . . . . . . 1454.2(3).1-6 Illustration of quantities involved in calculation of depth. . . . . . . . . . . . . . . . . . . . . . . . . . 1474.2(3).1-7 Flow diagram for water categories in the HYDR Section of the RCHRES Application Module . . . . . . . . . . . . 1514.2(3).2-1 Determination of weighting factors for advection calculations . . . . . . . . . . . . . . . . . . . . . . 1544.2(3).3-1 Flow diagram for conservative constituents in the CONS section of the RCHRES Application Module . . . . . . . . 1574.2(3).4-1 Flow diagram for HTRCH section of the RCHRES Application Module . . . . . . . . . . . . . . . . . . . . . . . . . 1624.2(3).5-1 Flow diagram of inorganic sediment fractions in the SEDTRN section of the RCHRES Application Module. . . . . 1704.2(3).5-2 Toffaleti's Velocity, Concentration, and Sediment Discharge Relations. . . . . . . . . . . . . . . . . . . 1774.2(3).5-3 Factors in Toffaleti Relations . . . . . . . . . . . . . 1804.2(3).5-4 Colby's Relationship for Discharge of Sands in Terms of Mean Velocity for Six Median Sizes of Bed Sands, Four Depths of Flow, and Water Temperature of 60 F . . . 1824.2(3).5-5 Colby's Correction Factors for Effect of Water Temperature, Concentration of Fine Sediment, and Sediment Size to be Applied to Uncorrected Discharge of Sand Given by Figure 4.2(3).5-4 . . . . . . 1834.2(3).6-1 Flow diagram for generalized quality constituent in the GQUAL section of the RCHRES Application Module . . . . . 1864.2(3).6-2 Simplified flow diagram for important fluxes and storages of sediment and associated qual used in subroutine ADVQAL. . . . . . . . . . . . . . . . . . . . 2014.2(3).7.1-1 Flow diagram for dissolved oxygen in the OXRX subroutine group of the RCHRES Application Module . . . . . . . . . 2104.2(3).7.1-2 Flow diagram for biochemical oxygen demand in the OXRX subroutine group of the RCHRES Application Module. . . . 2104.2(3).7.2-1 Flow diagram for inorganic nitrogen in the NUTRX subroutine group of the RCHRES Application Module. . . . 218 4.2(3).7.2-2 Flow diagram for ortho-phosphate in the NUTRX group of the RCHRES Application Module. . . . . . . . . . . . . . 2194.2(3).7.3-1 Flow diagram for phytoplankton in the PLANK section of the RCHRES Application Module. . . . . . . . . . . . . . 2314.2(3).7.3-2 Flow diagram for dead refractory organics in the PLANK section of the RCHRES Application Module . . . . . . . . 2314.2(3).7.3-3 Flow diagram for zooplankton in the PLANK section of the RCHRES Application Module. . . . . . . . . . . . . . . . 2324.2(3).7.3-4 Flow diagram for benthic algae in the PLANK section of the RCHRES Application Module. . . . . . . . . . . . . . 2324.2(3).7.3-5 Relationship of parameters for special advection of plankton . . . . . . . . . . . . . . . . . . . . . . . . 2354.2(3).7.3-6 Zooplankton assimilation efficiency. . . . . . . . . . . 246
Functional Description
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FIGURES (continued) Number Page
4.2(3).7.4-1 Flow diagram of inorganic carbon in the PHCARB group of the RCHRES Application Module . . . . . . . . . . . . 2554.2(12)-1 Sample PLOTFL. . . . . . . . . . . . . . . . . . . . . . 2624.2(13)-1 Sample Short-Span Display (First Type) . . . . . . . . . 2654.2(13)-2 Sample Short-Span Display (Second Type). . . . . . . . . 2664.2(13)-3 Sample Long-Span (Annual) Display. . . . . . . . . . . . 2674.2(14)-1 Definition of Terms Used in Duration Analysis Module . . 2694.2(14)-2 Sample Duration Analysis Printout. . . . . . . . . . . . 2714.2(14)-3 Sample Lethal Concentration (LC) Function for Global Exceedance Calculation. . . . . . . . . . . . . . 275
Functional Description
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GENERAL COMMENTS
For a discussion on how this part of the documentation is organized, refer toSection 4 in Part B "General Principles".
1.0 MAIN Program
The MAIN program calls, directly or indirectly, all the other modules in thesystem. The functions performed are:
1. Preprocess the Users Control Input (UCI). Subroutine USRRDR transfers the UCIto memory, sets up a pointer system to non-comment lines, and recognizes inputset headings and delimiters: RUN, END RUN.
2. If a RUN input set has been found, call subroutine INTERP to interpret it andthen call OSUPER to supervise execution of it.
2.0 Manage the Time Series Store (Omitted)
3.0 Interpret a RUN Data Set in the User's Control Input (Module INTERP)
General Description of Module INTERP
This module, known as the Run Interpreter, translates a RUN data set in the User'sControl Input (documented in Section 4 of Part F) into many elementaryinstructions, for later use by other parts of the system, when the time series areoperated on. To do this, the Run Interpreter performs such tasks as:
1. Check and augment the data supplied by the user.
2. Decide which time series will be required and produced by each operation,based on the user's data and built-in tables which contain information on thevarious operations.
3. Allocate INPAD rows to the various time series.
4. Read the control data, parameters, and initial conditions supplied for eachoperation, convert them to internal units, and supply default values whererequired.
The output of the Run Interpreter is stored in memory arrays containinginstructions to be read by the Operations Supervisor, TSGET and TSPUT (Figure3.0-1). The instruction arrays contain the following information:
Run Interpreter
31
Figure 3.0-1 Functions and data transfers involved in the operations portion of HSPF
Run Interpreter
32
1. The Operations Supervisor Instructions Array. This array containsinstructions which the Operations Supervisor reads to manage the operationsin a run. This includes information on:
a) the configuration of the scratch pads (time intervals and widths)
b) the configuration of the INGROUPs, such as the number of INGROUPs,operations in each INGROUP, etc.
2. The Operation Status Vector Array. The operations in a run are interruptedevery time an INPAD span is completed (Part B, Section 3.2). To save computermemory, the system is designed so that the various operations all use the samearea of memory. This requires that upon interruption of an operation, allinformation necessary to restart an operation be stored in a disk file oranother memory area. The data, called the "Operation Status Vector" (OSV),reside in a string of contiguous locations in memory and have a structurespecified in the Programmer's Supplement (Johanson, et al., 1979). The arrayOSVMEM contains an exact copy of the OSV for each operation. It is used torestore the OSV in the common memory area when the operation is resumed afterinterruption.
3. The Input Time Series Instruction Array (TSGETM) and the Output Time SeriesInstruction Array (TSPUTM). These arrays contain instructions which govern thetransfer of pieces of time series into and out of the INPAD, respectively.Each instruction enables module TSGET to retrieve a specified piece of timeseries from one of the source volumes (Figure 3.0-1), transform it to theinterval and form required for the INPAD, and insert it in the desired row ofthe INPAD. In the case of TSPUTM, the sequence is the reverse of that justdescribed.
Each operation has its own set of instructions in TSGETM and TSPUTM which areread whenever modules TSGET and TSPUT are called upon to service thatoperation (every INSPAN).
4. The Special Action Instruction Array (SPACM). Each record of this arraycontains a single special action instruction, which specifies the actionrequired to be taken in a given operation at a specific time, e.g., reportoperation state, modify a state variable.
4.0 Supervise and Perform Operations (module OSUPER)
Function of Operations Group
The Operations group of modules handles all the manipulations of time series andthus, performs most of the work in a run. Subroutine OSUPER controls the group.It performs some of the tasks itself, but it invokes subordinate modules to do other tasks.
Operations Group
33
General Description of Subroutine OSUPER The primary tasks of subroutine OSUPER are to ensure that the various operationsin the run are called in the correct sequence and that the associated time seriesand OSVs are input and/or output at the required junctures (see Part B, Section3.2). OSUPER uses a nest of DO-loops to control the sequencing. The instructionarray OSUPM specifies the ranges of the loops and supplies information ("keys")which enable OSUPER, TSGET and TSPUT to correctly access the other instructionarrays. OSUPER reads an instruction each time an operation starts a new INSPAN.Using this information, it then:
1. calls TSGET, to supply the required input time series
2. reads the OSV from disk or memory storage
3. calls the operating module
When the INSPAN is over, OSUPER:
1. writes the OSV to disk or memory storage
2. calls TSPUT, to output time series
4.03 Perform Special Actions (Subroutine SPECL)
HSPF permits the user to perform certain "Special Actions" during the course of arun. A special action instruction specifies the following:
1. The operation on which the action is to be performed (e.g., PERLND 10)
2. The date/time at which the action is to be taken.
3. The variable name and element (if the variable is an array) or the type andlocation within COMMON block SCRTCH of the data item to be updated.
4. The action to be performed. Two choices are available:
a) Reset the variable to a specified value
b) Increment the variable by a specified value
The special action facility is used to accommodate things such as:
1. Human intervention in a watershed. Events such as plowing, cultivation,fertilizer and pesticide application, and harvesting are simulated in thisway.
Operations Group
34
2. Changes to parameters. For example, a user may wish to alter the value of aparameter for which 12 monthly values cannot be supplied. This can be doneby specifying a special action for that variable. The parameter could bereset to its original value by specifying another special action, to be takenat a later time.
Special Actions can be performed on variables in the PERLND, IMPLND, RCHRES, COPY,PLTGEN, and GENER modules. The input is documented in Section 4.10 of Part F.
4.1 Get Required Input Time Series (module TSGET)
The task of this module is to insert in the INPAD all input time series requiredby an operation. OSUPER calls it each time an operation is to commence an INSPAN,passing to it the keys of the first and last records in the TSGET instruction arraywhich must be read and acted upon. Each instruction causes a row of the INPAD tobe filled. TSGET can draw its input time series from any of the following source"volumes": WDM file, DSS file, sequential file and INPAD (Figure 3.0-1).
TSGET will, if necessary, automatically transform the time interval and "kind"(Appendix V) of the time series, as it is transferred from the source location tothe INPAD (target). TSGET can also perform a linear transformation on the valuesin a time series; for example, if the source contains temperatures in degrees C andthe INPAD needs them in degrees F.
4.2 Perform an Operation
Function of an Operating Module
An operating module is at the center of every operation (Part B, Section 2.1).When the Operations Supervisor calls an operating module the time series which itrequires are already in the INPAD. The task of the operating module is to operateon these input time series. The results of this work are:
1. updated state variables. The operating module constantly updates any statevariables. These are located in the OSV. Thus, when the operating modulereturns control to the Operations Supervisor, which copies the OSV to disk ormemory storage area, the latest values of all state variables areautomatically preserved.
2. printed output. The operating module accumulates values, formats them androutes these data to the line printer.
3. output time series. The operating module places these in the INPAD, but is notconcerned with their ultimate disposition; this is handled by module TSPUT.
Operations Group
35
Note that all time series simultaneously present in an INPAD have the same constanttime interval. This implies that, internally, all time series involved in anoperation have the same time interval. Externally, the time series may havediffering time intervals. Part of the function of modules TSGET and TSPUT is toconvert time series from external to internal time intervals and vice versa.
Sub-divisions in an Operating Module
An operating module may be divided into several distinct sections, each of whichmay be selectively activated in a given run, under the user's control, e.g., thePervious Land-segment module (PERLND) contains twelve sections, the first being airtemperature correction, and the last is tracer (conservative substance) simulation.The operating procedure is as follows: in each time interval of the INSPAN, theoperating module calls each of its active sections in the order in which they arebuilt into the code (the sequence can not be altered by the user). When the INSPANhas been covered, the operating module returns control to OSUPER which determinesthe next action to be taken. This procedure implies that an operating module mustbe arranged so that a section is called after any others from which it requiresinformation. For example, in the Pervious Land-segment module, the sedimentcalculation section may use data computed by the snow and water balance sections,but not by sections listed after sediment. This kind of information flow is calledan inter-section data transfer (ISDT).
Partitioning of an Operation
A user may activate one group of module sections in an initial run and other groupsin subsequent runs. Thus, it is possible to "partition" an operation. Forexample, it is possible to calibrate the hydraulic response of a set of riverreaches before moving on to simulate the behavior of constituents contained in thewater. If this type of work involves ISDT's between the sections handled indifferent runs, it follows that:
1. The time series involved in the ISDT's must be stored between runs, probablyin the WDM file.
2. In the second run the system will expect the user to specify external sourcesfor all of these time series.
Some users will be confused by the rules for partitioning operations, but ourexperience indicates this will be outweighed by the flexibility which it brings.
Operations Group
36
Numbering of Operating Modules
In principle, there is no limit to the number of operating modules which the systemcan accommodate. Ultimately, we expect a large number of modules ranging from verysimple utility modules (e.g., COPY) to very complex simulation algorithms (e.g.,PERLND). Although the size and complexity of the modules vary greatly, they allare, logically, of equal rank (Figure 2-3, Part B). The adopted numbering systemreflects this. Every operating module is identified by the number 4.2 and isdistinguished from the others only by a subscript. For example, the PerviousLand-segment Module is 4.2(1) and the Reach/Mixed Reservoir Module 4.2(3).
Inserting Additional Operating Modules
A programmer may insert additional modules. This requires the following tasks:
1. Write or adapt the operating module. This includes restructuring the datainto an OSV which conforms with the requirements of the HSPF system.
2. Add a section of code to the Run Interpreter to interpret the UCI for the newmodule.
3. Add data to the message/information file (HSPFMSG.WDM).
4. Make minor changes to subroutines OPNBLK and OSUPER.
Types of Operating Modules
There are two types of operating modules; utility modules and application modules.Utility modules perform any operations involving time series which are essentiallyauxiliary to application operations, e.g., input time series data from ASCIIformatted files to the WDM file using COPY, multiply two time series together toobtain a third one, plot several time series on the same graph. Application(simulation) modules represent processes, or groups of processes, which occur inthe real world.
Module PERLND
37
4.2(1) Simulate a Pervious Land Segment (Module PERLND)
A land segment is a subdivision of the simulated watershed. The boundaries areestablished according to the user's needs, but generally, a segment is defined asan area with similar hydrologic characteristics. For modeling purposes, water,sediment, and water quality constituents leaving the watershed move laterally toa downslope segment or to a reach/reservoir. A segment of land which has thecapacity to allow enough infiltration to influence the water budget is consideredpervious. In HSPF, PERLND is the module that simulates the water quality andquantity processes which occur on a pervious land segment.
The primary module sections in PERLND simulate snow accumulation and melt (SectionSNOW), the water budget (section PWATER), sediment produced by land surface erosion(section SEDMNT), and water quality constituents by various methods (section PQUALand the agri-chemical sections). Other sections perform the auxiliary functionsof correcting air temperature (section ATEMP) for use in snowmelt and soiltemperature calculations, producing soil temperatures (section PSTEMP) forestimating the outflow temperatures and influencing reaction rates in theagri-chemical sections, and determining outflow temperatures which influence thesolubility of oxygen and carbon dioxide. The structure chart for the PERLND module(Figure 4.2(1)-1) shows these sections and their relationships to each other andto PPTOT, PBAROT, and PPRINT. These last three sections manipulate the dataproduced. Section PPTOT places state variables (point values) and PBAROT placesflux variables which are actually averages over the interval (mean values) into theINPAD. PPRINT produces the printable results in the quantity and frequency thatthe user specifies. The sections in the structure chart are executed from left toright.
Module PERLND
38
Figure 4.2(1)-1 Structure chart for PERLND Module
Module Section ATEMP
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4.2(1).1 Correct Air Temperature for Elevation Difference (Section ATEMP of Modules PERLND and IMPLND)
Purpose
The purpose of ATEMP is to modify the input air temperature to represent the meanair temperature over the land segment. This module section is used by both PERLNDand IMPLND. Air temperature correction is needed when the elevation of the landsegment is significantly different than the elevation at the temperature gage. Ifno correction for elevation is needed, this module section can be skipped.
Method
The lapse rate for air temperature is dependent upon precipitation during the timeinterval. If precipitation occurs, a wet lapse rate of 0.0035 degrees F per footdifference in elevation is assumed. Otherwise, a dry lapse rate, that varies withthe time of day, is used. A table of 24 hourly dry lapse rates varying between0.0035 to 0.005 is built into the system. A different, user-defined lapse rate maybe implemented by modifying the HSPF message/information file (HSPFMSG.WDM). Thecorrected air temperature is:
AIRTMP = GATMP - LAPS*ELDAT (1)
where: AIRTMP = corrected air temperature (degrees F) GATMP = air temperature at gage (degrees F) LAPS = lapse rate (degrees F/ft) ELDAT = elevation difference between the land segment and the gage (ft)
Module Section SNOW
40
4.2(1).2 Simulate Accumulation and Melting of Snow and Ice (section SNOW of modules PERLND and IMPLND)
Purpose
SNOW deals with the runoff derived from the fall, accumulation, and melt of snow.This is a necessary part of any complete hydrologic package since much of therunoff, especially in the northern half of the United States, is derived from snowconditions.
Approach
Figure 4.2(1).2-1 illustrates the processes involved in snow accumulation and melton a land segment. The algorithms used are based on the work by the Corps ofEngineers (1956), Anderson and Crawford (1964), and Anderson (1968). Empiricalrelationships are employed when physical ones are not well known. The snowalgorithms use meteorologic data to determine whether precipitation is rain orsnow, to simulate an energy balance for the snowpack, and to determine the effectof the heat fluxes on the snowpack.
Five meteorologic time series are required by SNOW for each land segment simulated.They are:
precipitation air temperature solar radiation dewpoint wind velocity A value from each of these time series is input to SNOW at the start of eachsimulation interval. However, some of the meteorological time series are only usedintermittently for calculating rates, such as in the calculation of the potentialrate of evaporation from the snowpack.
Air temperature is used to determine when snow is falling. Once snow begins toaccumulate on the ground, the snowpack accumulation and melt calculations takeplace. Five sources of heat which influence the melting of the snowpack aresimulated:
1. net radiation heat (RADHT), both longwave and shortwave 2. convection of sensible heat from the air (CONVHT) 3. latent heat transfer by condensation of moist air on the snowpack (CONDHT) 4. heat from rain, sensible heat from rain falling (RNSHT) and latent heat from
rain freezing on the snowpack 5. conduction of heat from the underlying ground to the snowpack (GMELTR)
Other heat exchange processes such as latent heat from evaporation are consideredless significant and are not simulated.
Module Section SNOW
41
Figure 4.2(1).2-1 Snow accumulation and melt proceses
Module Section SNOW
42
The energy calculations for RADHT, CONVHT, and CONDHT are performed by subroutineHEXCHR while GMELTR is calculated in subroutine GMELT. Latent heat from rainfreezing is considered in subroutine WARMUP. RNSHT is computed in the parentsubroutine SNOW. For uniformity and accounting, energy values are calculated interms of the water equivalent which they could melt. It takes 202.4 calories persquare cm on the surface to melt one inch water equivalent of snow at 32 degreesF. All the sources of heat including RNSHT are considered to be positive (incomingto the pack) or zero, except RADHT which can also be negative (leaving the pack).
Net incoming heat from the atmosphere (the sum of RADHT, CONVHT, CONDHT, and RNSHT)is used to warm the snowpack. The snowpack can be further warmed by the latentheat released upon rain freezing. Any excess heat above that required to warm thesnowpack to 32 degrees F is used to melt the pack. Likewise, net loss of heat isused to cool the snowpack producing a negative heat storage. Furthermore, incomingheat from the ground melts the snowpack from the bottom independent of theatmospheric heat sources except that the rate depends on the temperature of thesnowpack.
Figure 4.2(1).2.2 gives a schematic view of the moisture related processes modeledin section SNOW. Precipitation may fall as rain or snow on the snowpack or theground. Evaporation only occurs from the frozen portion of the pack (PACKF). Thefrozen portion of the pack is composed of snow and ice. The ice portion of PACKFis considered to be in the lower part of the snowpack, so it is the first to meltwhen heat is conducted from the ground. Similarly, the snow portion of PACKF isthe first to melt when atmospheric heat increases. Melted PACKF and rain fallingon the snowpack produce the water portion of the total snowpack which may overflowthe capacity of the pack. The water yield and rain on the bare ground becomesinput to module section PWATER or IWATER. These moisture related processes as wellas the heat exchange processes are discussed later in more detail.
Heat transfer from incoming rain (RNSHT) to the snowpack is calculated in theparent subroutine SNOW (Section 4.2(1).2). The following physically based equationis used:
RNSHT = (AIRTMP - 32.0)*RAINF/144.0 (2)
where: AIRTMP = temperature of the air (degrees F) RAINF = rainfall (inches) 144.0 = factor to convert to equivalent depth of melt 32.0 = freezing point (degrees F)
Other characteristics of the snowpack are also determined in the main subroutineSNOW. The fraction of the land segment covered by the snowpack is estimated bymerely dividing the depth of the snowpack by a cover index (COVINX) which is afunction of the parameter COVIND and the history of the pack as explained insubroutine EFFPRC. The temperature of the snowpack is estimated by: PAKTMP = 32.0 - NEGHTS/(0.00695*PACKF) (3)
Module Section SNOW
43
Figure 4.2(1).2-2 Flow diagram of water movement, storages, and phase changes modeled in the SNOW section of the PERLND and IMPLND Application Modules
Module Section SNOW
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where: PAKTMP = mean temperature of the snowpack (degrees F) NEGHTS = negative heat storage (inches of water equivalent) PACKF = frozen contents of the snowpack (inches of water equivalent) 0.00695 = physically based conversion factor
4.2(1).2.1 Estimate Meteorological Conditions (subroutine METEOR)
Purpose
Subroutine METEOR estimates the effects of certain meteorological conditions onspecific snow-related processes by the use of empirical equations. It determineswhether precipitation is falling as snow or rain. The form of precipitation iscritical to the reliable simulation of runoff and snowmelt. When snow is falling,the density is calculated in order to estimate the depth of the new snowpack. Thefraction of the sky which is clear is also estimated for use in the radiationalgorithms, and the gage dewpoint temperature is corrected if it is warmer than airtemperature.
Method
The following expression is used to calculate hourly the effective air temperaturebelow which snowfall occurs: SNOTMP = TSNOW + (AIRTMP - DEWTMP)*(0.12 + 0.008*AIRTMP) (4) where: SNOTMP = air temperature below which snowfall occurs (degrees F) TSNOW = parameter (degrees F) AIRTMP = air temperature (degrees F) DEWTMP = dewpoint (degrees F) SNOTMP is allowed to vary in this calculation by a maximum of one degree F fromTSNOW. When AIRTMP is equal to or greater than SNOTMP, precipitation is assumedto be rain.
When snowfall occurs, its density is estimated as a function of air temperatureaccording to: RDNSN = RDCSN + (AIRTMP/100.0)**2 (5)
where: RDNSN = density of new snowfall (at zero degrees F or greater) relative to liquid water RDCSN = parameter designating density of new snow at an air temperature of zero degrees F and lower, relative to liquid water
Module Section SNOW
45
RDNSN is used in subroutine EFFPRC to calculate the new depth of the snowpackresulting from the addition of the snow. This and all other snow density terms arein water equivalent (inches) per depth of the snowpack (inches).
The fraction of the sky which is clear (SKYCLR) is needed for the calculation ofthe longwave back radiation to the snowpack from the clouds (done in subroutineHEXCHR). SKYCLR is set to the minimum value of 0.15 when precipitation occurs.Otherwise, it is increased each simulation time interval as follows:
SKYCLR = SKYCLR + (0.0004*DELT) (6) where: DELT = simulation time interval (min)
SKYCLR increases until either it reaches unity or precipitation causes it to bereset.
A gage dewpoint higher than air temperature is not physically possible and willgive erroneous results in the calculation of snowpack evaporation. Therefore,dewpoint is set equal to the air temperature when this situation occurs.Otherwise, the gage dewpoint is used.
4.2(1).2.2 Determine the Effect of Precipitation on the Pack (subroutine EFFPRC)
Purpose
The purpose of this subroutine is to add the falling snow to the pack, determinethe amount of rain falling on the snowpack, and adjust the snowpack dullness totake into account new snow.
Method
The amount of precipitation falling as snow or rain is determined in subroutineMETEOR. Subroutine EFFPRC accounts for the influence that snowfall and rain haveon the land segment. The subroutine begins by increasing the snowpack depth by theamount of snow falling on the pack divided by its density.
The fraction of the land segment which is a covered by the snowpack (SNOCOV) isdetermined by re-evaluating the index to areal coverage (COVINX). When the frozencontents of the pack (PACKF) exceeds the value of the parameter describing themaximum PACKF required to insure complete areal coverage by snow cover (COVIND),then COVINX is set equal to COVIND. Otherwise, COVINX is equal to the largestprevious value of PACKF. SNOCOV is PACKF/COVINX if PACKF < COVINX. The amount ofrain falling on the snowpack is that fraction of the precipitation which falls asrain multiplied by the SNOCOV. Rain falling on the snowpack will either freeze,adding to the frozen portion of the pack and produce heat used to warm the pack(see subroutine WARMUP), or it will increase the liquid water content of the pack(see subroutine LIQUID). Any rain not falling on the pack is assumed to land onbare ground.
Module Section SNOW
46
When snowfall occurs, the index to the dullness of the snowpack (DULL) is decreasedby one thousand times the snowfall for that interval. However, if one thousandtimes the snowfall is greater than the previous value for DULL, then DULL is setto zero to account for a new layer of perfectly reflectable snow. Otherwise, whensnowfall does not occur, DULL is increased by one index unit per hour up to amaximum of 800. Since DULL is an empirical term used as an index, it has nophysical units. DULL is used to determine the albedo of the snowpack, which inturn is used in the shortwave energy calculations in subroutine HEXCHR.
4.2(1).2.3 Compact the Pack (subroutine COMPAC)
Purpose
The addition of new snow will reduce the density as well as increase the depth ofthe snowpack as in subroutine EFFPRC. The pack will tend to compact with age untila maximum density is reached. The purpose of subroutine COMPAC is to determine therate of compaction and calculate the actual change in the depth due to compaction.
Method
When the relative density is less than 55 percent, compaction is assumed to occur.The rate of compaction is computed according to the empirical expression:
COMPCT = 1.0 - (0.00002*DELT60*PDEPTH*(0.55 - RDENPF)) (7)
where: COMPCT = unit rate of compaction of the snowpack per interval DELT60 = number of hours in an interval PDEPTH = depth of the snowpack in inches of total snowpack RDENPF = density of the pack relative to liquid water
The new value for PDEPTH is COMPCT times PDEPTH. PDEPTH is used to calculate therelative density of the snowpack which affects the liquid water holding capacityas determined in subroutine LIQUID.
4.2(1).2.4 Simulate Evaporation from the Pack (subroutine SNOWEV)
Purpose
The SNOWEV subroutine estimates evaporation from the snowpack (sublimation).
Method
Evaporation from the snowpack will occur only when the vapor pressure of the airis less than that of the snow surface, that is, only when the air vapor pressureis less than 6.108 mbar, which is the maximum vapor pressure that the thin surfacefilm of air over the snowpack can attain. When this condition is met theevaporation is computed by the empirical relationship:
Module Section SNOW
47
SNOWEP = SNOEVP*0.0002*WINMOV*(SATVAP - VAP)*SNOCOV (8)
where: SNOWEP = potential rate of evaporation from the frozen part of the snowpack (inches of water equivalent/interval) SNOEVP = parameter used to adjust the calculation to field conditions WINMOV = wind movement (miles/interval) SATVAP = saturated vapor pressure of the air at the current air temperature (mbar) VAP = vapor pressure of the air at the current air temp (mbar) SNOCOV = fraction of the land segment covered by the snowpack
The potential (SNOWEP) will be fulfilled if there is sufficient snowpack.Otherwise, only the remaining pack will evaporate. For either case, evaporationoccurs only from the frozen content of the snowpack (PACKF).
4.2(1).2.5 Estimate Heat Exchange Rates (except ground melt and rain heat) (subroutine HEXCHR)
Purpose
The purpose of this subroutine is to estimate the heat exchange from the atmospheredue to condensation, convection, and radiation. All heat exchanges are calculatedin terms of equivalent depth of melted or frozen water.
Method of Determining Heat Supplied by Condensation
Transfer of latent heat of condensation can be important when warm moist air massestravel over the snowpack. Condensation occurs when the air is moist enough tocondense on the snowpack. That is, when the vapor pressure of the air is greaterthan 6.108 mbar. This physical process is the opposite of snow evaporation; theheat produced by it is calculated by another empirical relationship:
CONDHT = 8.59*(VAP - 6.108)*CCFACT*0.00026*WINMOV (9) where: CONDHT = condensation heat flux to the snowpack (inches of water equivalent/interval) VAP = vapor pressure of the air at the current air temp (mbar) CCFACT = parameter used to correct melt values to field conditions WINMOV = wind movement (miles/interval)
CONDHT can only be positive or zero, that is, incoming to the pack.
Module Section SNOW
48
Method of Determining Heat Supplied by Convection
Heat supplied by turbulent exchange with the atmosphere can occur only when airtemperatures are greater than freezing. This convection of heat is calculated bythe empirical expression:
CONVHT = (AIRTMP - 32.0)*(1.0 - 0.3*MELEV/10000.0)* (10) CCFACT*0.00026*WINMOV
where: CONVHT = convective heat flux to the snowpack (inches of water equivalent/interval) AIRTMP = air temperature (degrees F) MELEV = mean elevation of the land segment above sea level (ft) In the simulation, CONVHT also can only be positive or zero, that is, onlyincoming.
Method of Determining Heat Supplied by Radiation
Heat supplied by radiation is determined by:
RADHT = (SHORT + LONG)/203.2 (11)
where: RADHT = radiation heat flux to the snowpack (inches of water equivalent/interval) SHORT = net solar or shortwave radiation (langleys/interval) LONG = net terrestrial or longwave radiation (langleys/interval) The constant 203.2 is the number of langleys required to produce one inch of meltfrom snow at 32 degrees F. RADHT can be either positive or negative, that is,incoming or outgoing.
SHORT and LONG are calculated as follows. Solar radiation, a required time series,is modified by the albedo and the effect of shading. The albedo or reflectivityof the snowpack is a function of the dullness of the pack (see subroutine EFFPRCfor a discussion of DULL) and the season. The equation for calculating albedo(ALBEDO) for the 6 summer months is:
ALBEDO = 0.80 - 0.10*(DULL/24.0)**0.5 (12)
The corresponding equation for the winter months is:
ALBEDO = 0.85 - 0.07*(DULL/24.0)**0.5 (13)
ALBEDO is allowed a minimum value of 0.45 for summer and 0.60 for winter. Thehemispheric location of the land segment is taken into account for determiningsummer and winter in using the above equation. This is done through the use of thelatitude parameter which is positive for the northern hemisphere.
Module Section SNOW
49
Once the albedo of the pack is found then solar radiation (SHORT) is modifiedaccording to the equation:
SHORT = SOLRAD*(1.0 - ALBEDO)*(1.0 - SHADE) (14) where: SOLRAD = solar radiation (langleys/interval) SHADE = parameter indicating the fraction of the land segment which is shaded Unlike shortwave radiation which is more commonly measured, longwave radiation(LONG) is estimated from theoretical consideration of the emitting properties ofthe snowpack and its environment. The following equations are based on Stefan'slaw of black body radiation and are linear approximations of curves in Plate 5-3,Figure 6 in Snow Hydrology (Corps of Engineers, 1956). They vary only by theconstants which depend on air temperature. For air temperatures above freezing:
LONG = SHADE*0.26*RELTMP + (1.0 - SHADE)*(0.2*RELTMP - 6.6) (15) And for air temperatures at freezing and below: LONG = SHADE*0.20*RELTMP + (1.0 - SHADE)*(0.17*RELTMP - 6.6) (16) where: RELTMP = air temperature minus 32 (degrees F) 6.6 = average back radiation lost from the snowpack in open areas (langleys/hr)
Since the constants in these equations were originally based on hourly time steps,both calculated values are multiplied by DELT60, the number of hours per interval,so that they correspond to the simulation interval. In addition, LONG ismultiplied by the fraction of clear sky (SKYCLR) when it is negative, to accountfor back radiation from clouds.
4.2(1).2.6 Simulate Loss of Heat from Pack (subroutine COOLER)
Purpose
The purpose of this code is to cool the snowpack whenever it is warmer than theambient air and thus loses heat. This is accomplished by accumulating negativeheat storage which increases the capacity of the pack to later absorb heat withoutmelting as simulated in subroutine WARMUP.
Module Section SNOW
50
Method
In every interval where there is heat loss to the atmosphere and the temperatureof the snowpack is greater than the air temperature, the negative heat storage willincrease; that is, the pack will cool. However, there is a maximum negative heatstorage. The maximum negative heat storage that can exist at any time is found byassuming a linear temperature distribution from the air temperature which isconsidered to be above the pack to 32 degrees at the bottom of the snowpack. Thismaximum negative heat storage is calculated hourly as follows:
MNEGHS = 0.00695*(PACKF/2.0)*(-RELTMP) (17) where: MNEGHS = maximum negative heat storage (inches of water equivalent) PACKF = water equivalent of the frozen contents of the snowpack (inches) RELTMP = air temperature above freezing (degrees F) The accumulation of the negative heat storage is calculated hourly from thefollowing empirical relationship:
NEGHT = 0.0007*(PAKTMP - AIRTMP)*DELT60 (18) where: NEGHT = potential rate of cooling of the snowpack (inches of water equivalent per interval) PAKTMP = mean temperature of the snowpack (degrees F) AIRTMP = air temperature (degrees F) DELT60 = number of hours per interval
NEGHT is added to the negative heat storage (NEGHTS) every interval except whenlimited by MNEGHS. NEGHTS is used in the parent subroutine SNOW to calculate thetemperature of the snowpack and in subroutine WARMUP to determine the extent thatthe pack must be warmed to reach 32 degrees F.
4.2(1).2.7 Warm the Snowpack if Possible (subroutine WARMUP)
Purpose This subroutine warms the snowpack to as much as 32 degrees F when possible.
Method
When there is negative heat storage in the pack (see subroutine COOLER for adiscussion of NEGHTS), and there is net incoming energy as calculated in previoussubroutines, then NEGHTS will decrease resulting in a warmer snowpack and possiblemelt.
Module Section SNOW
51
The calculations in this subroutine are merely accounting. They decrease NEGHTSto a minimum of zero by subtracting the net incoming heat. If any negative heatstorage remains, then the latent heat released by the freezing of any incoming rainis added to the pack. Since NEGHTS and all other heat variables are in units ofinches of melt, the inches of rain falling on the pack and freezing is subtractedfrom NEGHTS without any conversion.
4.2(1).2.8 Melt the Pack Using Any Remaining Heat (subroutine MELTER)
Purpose
MELTER simulates the actual melting of the pack with whatever incoming heatremains. Any heat which was not used to heat the snowpack in subroutine WARMUP cannow be used to melt the snowpack.
Method
This subroutine is also merely an accounting subroutine. The net incoming heat hasalready been calculated in terms of water equivalents of melt. Hence, anyremaining incoming heat is used directly to melt the snowpack either partially orentirely depending on the size of the snowpack.
4.2(1).2.9 Handle Liquid Water in the Pack (subroutine LIQUID)
Purpose
Subroutine LIQUID first determines the liquid storage capacity of the snowpack.It then determines how much liquid water is available to fill the storage capacity.Any liquid water above the capacity will leave the snowpack unless it freezes (seesubroutine ICING).
Method
The liquid water holding capacity of the snowpack can be at the maximum asspecified by the parameter MWATER, at zero, or somewhere in between depending onthe density of the pack: the less dense the snowpack the greater the holdingcapacity. The following relationships define the capacity:
for RDENPF > 0.91,
PACKWC = 0.0 (19)
for 0.6 < RDENPF < 0.91, PACKWC = MWATER*(3.0 - 3.33*RDENPF) (20)
Module Section SNOW
52
for RDENPF < 0.61,
PACKWC = MWATER (21)
where: PACKWC = liquid water holding capacity of the snowpack (in/in) MWATER = parameter specifying the maximum liquid water content of the snowpack (in/in) RDENPF = density of the snowpack relative to liquid water
MWATER is a function of the mass of ice layers, the size, the shape, and spacingof snow crystals and the degree of channelization and honeycombing of the snowpack.
Once PACKWC is calculated, it is compared to the available liquid water in the packPWSUPY. PWSUPY is calculated by summing any storage remaining at the start of theinterval, any melt, and any rain that fell on the pack which did not freeze. IfPWSUPY is more than PACKWC, then water is yielded to the land surface from thesnowpack.
4.2(1).2.10 Simulate Occurrence of Ice in the Pack (subroutine ICING)
Purpose
The purpose of subroutine ICING is to simulate the possible freezing of water whichwould otherwise leave the snowpack. This freezing in turn produces ice or frozenground at the bottom of the snowpack. In this subroutine, the ice can beconsidered to be at the bottom of the pack or frozen in the ground below the snowportion of the pack thus extending the total pack into the soil. This subroutinemay only be applicable in certain areas; therefore, it is optional.
Method
The freezing of the water yield of the snowpack depends on the capacity of theenvironment to freeze it. Every day at approximately 6 a.m. the capacity isreassessed. A new value is estimated in terms of inches of melt by multiplying theFahrenheit degrees of the air temperature below 32.0 by 0.01. This estimate iscompared with the freezing capacity if any which remains from the previous 24-hrperiod. If it is greater, then the new estimated capacity replaces the old, elsethe old value remains as the potential. Any water yield that occurs freezes andis added to the ice portion of the snowpack until the capacity is met. Anysubsequent water yield is released from the snowpack.
Module Section SNOW
53
4.2(1).2.11 Melt the Pack Using Heat from the Ground (subroutine GMELT)
Purpose
The purpose of the GMELT subroutine is to simulate the melt caused by heatconducted from the surface underlying the snowpack. This ground heat melts the packonly from below. Therefore, melt from this process is considered independent ofother previously calculated heat influences except for an indirect effect via thetemperature of the snowpack. Unlike the other melt processes, ground heat melts theice portion of the snowpack first since ice is considered to be located at thelower depths of the pack.
Method
The potential rate of ground melt is calculated hourly as a function of snowpacktemperature (PAKTMP) and a lumped parameter (MGMELT). MGMELT is the maximum rateof melt in water equivalent caused by heat from the ground at a PAKTMP of 32degrees F. MGMELT would depend upon the thermal conductivity of the soil and thenormal depth of soil freezing. The potential ground melt is reduced below MGMELTby 3 percent for each degree that PAKTMP is below 32 degrees F to a minimum of 19percent of MGMELT at 5 degrees F or lower. As long as a snowpack is present, groundmelt occurs at this potential rate.
4.2(1).2.12 Reset State Variables When Snowpack Disappears (subroutine NOPACK)
Purpose
This code resets the state variables (for example, SNOCOV) when the snowpackcompletely disappears.
Method
The frozen contents of the snowpack required for complete areal cover of snow(COVINX) is set to a tenth of the maximum value (COVIND). All other variables areeither set to zero or the "undefined" value of -1.0E30.
Module Section PWATER
54
4.2(1).3 Simulate Water Budget for a Pervious Land Segment (Section PWATER of Module PERLND)
Purpose
PWATER is used to calculate the components of the water budget, primarily topredict the total runoff from a pervious area. PWATER is the key component ofmodule PERLND; subsequent major sections of PERLND (eg. SEDMNT) depend on theoutputs of this section.
Background
The hydrologic processes that are modeled by PWATER are illustrated in Figure4.2(1).3-1. The algorithms used to simulate these land related processes are theproduct of over 15 years of research and testing. They are based on the originalresearch for the LANDS subprogram of the Stanford Watershed Model IV (Crawford andLinsley, 1966). LANDS has been incorporated into many models and used tosuccessfully simulate the hydrologic responses of widely varying watersheds. Theequations used in module section PWATER are nearly identical to the ones in thecurrent version of LANDS in the PTR Model (Crawford and Donigian, 1973), HSP(Hydrocomp, 1976), and the ARM and NPS Models (Donigian and Crawford, 1976 a,b).However, some changes have been made to LANDS to make the algorithms internallymore amenable to a range of calculation time steps. Also, many of the parameternames have been changed to make them more descriptive, and some can be input on amonthly basis to allow for seasonal variation.
Data Requirements and Manipulation
The number of time series required by module section PWATER depends on whether snowaccumulation and melt are considered. When such conditions are not considered,only potential evapotranspiration and precipitation are required. However, whensnow conditions are considered, air temperature, rainfall, snow cover, water yield,and ice content of the snowpack are also required. Also, the evaporation data areadjusted when snow is considered. The input evaporation values are reduced toaccount for the fraction of the land segment covered by the snowpack (determinedfrom the generated time series for snow cover), with an allowance for the fractionof area covered by coniferous forest which, it is assumed, can transpire throughany snow cover. Furthermore, PET is reduced to zero when air temperature is belowthe parameter PETMIN. If air temperature is below PETMAX but above PETMIN, PETwill be reduced to 50% of the input value, unless the first adjustment alreadyreduced it to less than this amount.
The estimated potential evapotranspiration (PET) is used to calculate actual ET insubroutine group EVAPT.
Module Section PWATER
55
Figure 4.2(1).3-1 Hydrologic cycle
Module Section PWATER
56
Approach
Figure 4.2(1).3-2 represents the fluxes and storages simulated in module sectionPWATER. The time series SUPY representing moisture supplied to the land segmentincludes rain, and when snow conditions are considered, rain plus water from thesnowpack. SUPY is then available for interception. Interception storage is waterretained by any storage above the overland flow plane. For pervious areas,interception storage is mostly on vegetation. Any overflow from interceptionstorage is added to the optionally supplied time series of surface external lateralinflow to produce the total inflow into the surface detention storage.
Inflow to the surface detention storage is added to existing storage to make up thewater available for infiltration and runoff. Moisture which directly infiltratesmoves to the lower zone and groundwater storages. Other water may go to the upperzone storage, may be routed as runoff from surface detention or interflow storage,or may stay on the overland flow plane, from which it runs off or infiltrates ata later time.
The processes of infiltration and overland flow interact and occur simultaneouslyin nature. Surface conditions such as heavy turf on mild slopes restrict thevelocity of overland flow and reduce the total quantity of runoff by allowing moretime for infiltration. Increased soil moisture due to prolonged infiltration willin time reduce the infiltration rate producing more overland flow. Surfacedetention will modify flow. For example, high intensity rainfall is attenuated bystorage and the maximum outflow rate is reduced. The water in the surfacedetention may also later infiltrate reoccurring as interflow, or it can becontained in upper zone storage.
Water infiltrating through the surface and percolating from the upper zone storageto the lower zone storage may flow to active groundwater storage or may be lost bydeep percolation. Active groundwater eventually reappears as baseflow, but deeppercolation is considered lost from the simulated system.
Lateral external inflows to interflow and active groundwater storages are alsopossible in section PWATER. One may wish to use this option if an upslope landsegment is significantly different to merit separating it from a downslope landsegment and no channel exists between them.
Not only are flows important in the simulation of the water budget, but so arestorages. As stated, soil storage affects infiltration. The water holdingcapacity of the two soil storages, upper zone and lower zone, in module sectionPERLND is defined in terms of nominal capacities. Nominal, rather than absolutecapacities, serve the purpose of smoothing any abrupt change that would occur ifan absolute capacity is reached. Such capacities permit a smooth transition inhydrologic performance as the water content fluctuates.
Module Section PWATER
57
Figure 4.2(1).3-2 Flow diagram of water movement and storages modeled in the PWATER section of the PERLND Application Module
Module Section PWATER
58
Figure 4.2(1).3-2 Flow diagram of water movement and storages modeled in the PWATER section of the PERLND Application Module (continued)
Module Section PWATER
59
Storages also affect evapotranspiration loss. Evapotranspiration can be simulatedfrom interception storage, upper and lower zone storages, active groundwaterstorage, and directly from baseflow.
Storages and flows can also be instrumental in the transformation and movement ofchemicals simulated in the agri-chemical module sections. Soil moisture levelsaffect the adsorption and transformations of pesticides and nutrients. Soilmoisture contents may vary greatly over a land segment. Therefore, a more detailedrepresentation of the moisture contents and fluxes may be needed to simulate thetransport and reaction of agricultural chemicals.
The following subroutine descriptions explain the algorithms of the PWATER modulesection in more detail. Further detail can be found in the reports cited above.
4.2(1).3.1 Simulate Interception (subroutine ICEPT)
Purpose
The purpose of this code is to simulate the interception of moisture by vegetal orother ground cover. Moisture is supplied by precipitation, or under snowconditions, it is supplied by the rain not falling on the snowpack plus the wateryielded by the snowpack.
Method
The user may supply the interception capacity on a monthly basis to account forseasonal variations, or may supply one value designating a fixed capacity. Theinterception capacity parameter can be used to designate any retention of moisturewhich does not infiltrate or reach the overland flow plane. Typically for perviousareas this capacity represents storage on grass blades, leaves, branches, trunks,and stems of vegetation.
Moisture exceeding the interception capacity overflows the storage and is ready foreither infiltration or runoff as determined by subroutine group SURFAC. Water heldin interception storage is removed by evaporation; the amount is determined insubroutine EVICEP.
Module Section PWATER
60
4.2(1).3.2 Distribute the Water Available for Infiltration and Runoff (subroutine SURFAC)
Purpose
Subroutine SURFAC determines what happens to the moisture on the surface of theland. It may infiltrate, go to the upper zone storage or interflow storage, remainin surface detention storage, or run off.
Method
The algorithms which simulate infiltration represent both the continuous variationof infiltration rate with time as a function of soil moisture and the arealvariation of infiltration over the land segment. The equations representing thedependence of infiltration on soil moisture are based on the work of Philip (1957)and are derived in detail in the previously cited reports.
The infiltration capacity, the maximum rate at which soil will accept infiltration,is a function of both the fixed and variable characteristics of the watershed.Fixed characteristics include primarily soil permeability and land slopes, whilevariables are soil surface conditions and soil moisture content. Fixed andvariable characteristics vary spatially over the land segment. A linear probabilitydensity function is used to account for areal variation. Figure 4.2(1).3-3represents the infiltration/interflow/surface runoff distribution function ofsection PWATER. Careful attention to this figure and Figure 4.2(1).3-2 willfacilitate understanding of subroutine SURFAC and the subordinate subroutinesDISPOS, DIVISN, UZINF, and PROUTE. The infiltration distribution represented by Figure 4.2(1).3-3 is focused aroundthe two lines which separate the moisture available to the land surface (MSUPY)into what infiltrates and what goes to interflow. A number of the variables thatare used to determine the location of lines I and II are calculated in subroutineSURFAC. They are calculated by the following relationships:
IBAR = (INFILT/(LZS/LZSN)**INFEXP)*INFFAC (1)
IMAX = INFILD*IBAR (2)
IMIN = IBAR - (IMAX - IBAR) (3)
RATIO = INTFW*(2.0**(LZS/LZSN)) (4)
where: IBAR = mean infiltration capacity over the land segment (in/interval) INFILT = infiltration parameter (in/interval)
Module Section PWATER
61
Figure 4.2(1).3-3 Determination of infiltration and interflow inflow
Module Section PWATER
62
LZS = lower zone storage (inches) LZSN = parameter for lower zone nominal storage (inches) INFEXP = exponent parameter greater than one INFFAC = factor to account for frozen ground effects, if applicable IMAX = maximum infiltration capacity (in/interval) INFILD = parameter giving the ratio of maximum to mean infiltration capacity over the land segment IMIN = minimum infiltration capacity (in/interval) RATIO = ratio of the ordinates of line II to line I INTFW = interflow inflow parameter
The parameter INTFW can be input on a monthly basis to allow for variationthroughout the year.
The factor that reduces infiltration (and also upper zone percolation) to accountfor the freezing of the ground surface (INFFAC) is calculated in one of two ways.In the first method, it is derived from the water equivalent of ice in the snowpackaccording to the equation:
INFFAC = 1.0- FZG*PACKI
where: FZG = parameter indicating how much icing reduces infiltration (/inches) PACKI = water equivalent of ice in snowpack (inches)
In this method, INFFAC is subject to a minimum, supplied as the dimensionlessparameter FZGL.
The second method determines INFFAC according to the soil temperature in the lowerlayer. If this temperature is less than 0 degrees C, then INFFAC is set to theparameter FZGL; otherwise it is set to 1.0. This method can only be used ifsection PSTEMP is active.
4.2(1)3.2.1 Dispose of Moisture Supply (subroutine DISPOS)
Purpose
Subroutine DISPOS determines what happens to the moisture supply (MSUPY) on theland segment.
Method
This subroutine calls subordinate routines DIVISN, UZINF, and PROUTE. DIVISN iscalled to determine how much of MSUPY falls above and below line I in Figure4.2(1).3-3. The quantity under this line is considered to be infiltrated. Theamount over the line but under the MSUPY line (the entire shaded portion) is thepotential direct runoff (PDRO), which is the combined increment to interflow, andupper zone storage plus the quantities which will stay on the surface and run off.PDRO is subdivided by line II. The ordinates of line II are found by multiplyingthe ordinates of line I by RATIO (see subroutine SURFAC for definition). The
Module Section PWATER
63
quantity underneath both line II and the MSUPY line but above line I is calledpotential interflow inflow. This consists of actual interflow plus an incrementto upper zone storage. Any amount above line II but below the MSUPY (potentialsurface detention/runoff) is that portion of the moisture supply which stays on thesurface and is available for overland flow routing, plus a further increment toupper zone storage. The fractions of the potential interflow inflow and potentialsurface detention/runoff which are combined to compose the upper zone inflow aredetermined in subroutine UZINF.
4.2(1).3.2.1.2 Compute Inflow to Upper Zone (subroutines UZINF1 and UZINF2)
Purpose
The purpose of this code is to compute the inflow to the upper zone when there issome potential direct runoff (PDRO). PDRO, which is determined in subroutineDISPOS, will either enter the upper zone storage or be available for eitherinterflow or overland flow. This subroutine determines what amount, if any, willgo to the upper zone storage.
Method
The fraction of the potential direct runoff which becomes inflow to the upper zonestorage is a function of the ratio (UZRAT) of the storage to the nominal capacity.Figure 4.2(1).3-4 diagrams this relationship. The equations used to define thiscurve follow:
FRAC = 1 - (UZRAT/2)*(1/(4 - UZRAT))**(3 - UZRAT) (7) for UZRAT less than or equal to two. For UZRAT greater than two, FRAC = (0.5/(UZRAT-1))**(2*UZRAT-3) (8) where: FRAC = fraction of PDRO retained by the upper zone storage UZRAT = UZS/UZSN
Since UZS and FRAC are dynamically affected by the inflow process it becomesdesirable when using particularly large time steps to integrate over the intervalto find the inflow to the upper zone. This is done in subroutine UZINF1. Thesolution is simplified by assuming that inflow to and outflow from the upper zoneare handled separately. Considering inflow, the following differential equationresults:
d(UZS)/dt = (d(UZRAT)/dt)*UZSN = PDRO*FRAC (9) Thus d(UZRAT)/FRAC = (PDRO/UZSN)*dt (10)
Module Section PWATER
64
Now taking the definite integral of both sides of the equation:
UZRATt2
! d(UZRAT) INTGRL = = --------- = (PDRO/UZSN)(t2-t1) (11) " FRAC UZRATt1
where: t1 = time at start of interval t2 = time at end of interval
The integral on the left side must be evaluated numerically. Subroutine UZINF1uses tabulated corresponding values of INTGRL and UZRAT to evaluate it. Thisrelationship, plus Equations 9 and 11, enable one to find the change in UZRAT overthe interval, and hence, the quantity of inflow.
Subroutine UZINF2, which is an alternative to UZINF1, uses the algorithm used inthe predecessor models HSP, ARM and NPS. That is, Equations 7 and 8 are useddirectly to estimate the fraction of PDRO retained by the upper zone. Only thevalue of UZRAT at the start of the simulation interval is used; thus, no accountis taken of the possible steady reduction in inflow to the upper zone within asingle time step, due to its being filled (Figure 4.2(1).3-4).
4.2(1).3.2.1.3 Determine Surface Runoff (subroutine PROUTE)
Purpose
The purpose of subroutine PROUTE is to determine how much potential surfacedetention runs off in one simulation interval.
Method of Routing
Overland flow is treated as a turbulent flow process. It is simulated using theChezy-Manning equation and an empirical expression which relates outflow depth todetention storage. A more detailed explanation and derivation can be found in thereports cited in the initial background discussion. The rate of overland flowdischarge is determined by the equations:
for SURSM < SURSE
SURO = DELT60*SRC*(SURSM*(1.0 + 0.6(SURSM/SURSE)**3)**1.67 (12)
for SURSM >= SURSE
SURO = DELT60*SRC*(SURSM*1.6)**1.67
Module Section PWATER
65
Figure 4.2(1).3-4 Fraction of the potential direct runoff retained by the upper zone (FRAC) as a function of the upper zone soil moisture ratio (UZRAT)
Module Section PWATER
66
where: SURO = surface outflow (in/interval) DELT60 = DELT/60.0 (hr/interval) SRC = routing variable, described below SURSM = mean surface detention storage over the time interval (in) SURSE = equilibrium surface detention storage (inches) for current supply rate
DELT60 makes the equations applicable to a range of time steps (DELT). The firstequation represents the case where the overland flow rate is increasing, and thesecond case where the surface is at equilibrium or receding. Equilibrium surfacedetention storage is calculated by:
SURSE = DEC*SSUPR**0.6 (13)
where: DEC = calculated routing variable, described below SSUPR = rate of moisture supply to the overland flow surface
There are two optional ways of determining SSUPR and SURSM. One option - the samemethod used in the predecessor models - HSP, ARM, and NPS - estimates SSUPR bysubtracting the surface storage at the start of the interval (SURS) from thepotential surface detention (PSUR) which was determined in subroutine DISPOS. Theunits of SSUPR are inches per interval. SURSM is estimated as the mean of SURS andPSUR. The other option estimates SSUPR by the same method except that the resultis divided by DELT60 to obtain a value with units of inches per hour. SURSM is setequal to SURS. This option is dimensionally consistent for any time step.
The variables DEC and SRC are calculated daily in subroutine SURFAC, but theirequations will be given here since they pertain to routing. They are:
DEC = 0.00982*(NSUR*LSUR/SQRT(SLSUR))**0.6 (14)
SRC = 1020.0*(SQRT(SLSUR)/(NSUR*LSUR)) (15)
where: NSUR = Manning's n for the overland flow plane LSUR = length of the overland flow plane (ft) SLSUR = slope of the overland flow plane (ft/ft)
NSUR can be input on a monthly basis to allow for variations in roughness of theoverland flow plane throughout the year.
Module Section PWATER
67
4.2(1).3.3 Simulate Interflow (subroutine INTFLW)
Purpose
Interflow can have an important influence on storm hydrographs particularly whenvertical percolation is retarded by a shallow, less permeable soil layer.Additions to the interflow component are retained in storage or routed as outflowfrom the land segment. Inflows to the interflow component may occur from thesurface or from upslope external lateral flows. The purpose of this subroutine isto determine the amount of interflow and to update the storage.
Method of Determining Interflow
The calculation of interflow outflow assumes a linear relationship to storage. Thusoutflow is a function of a recession parameter, inflow, and storage. Moisture thatremains will occupy interflow storage. Interflow discharge is calculated by:
IFWO = (IFWK1*INFLO) + (IFWK2*IFWS) (16)
where: IFWO = interflow outflow (in/interval) INFLO = inflow into interflow storage (in/interval) IFWS = interflow storage at the start of the interval (inches)
IFWK1 and IFWK2 are variables determined by:
IFWK1 = 1.0 - (IFWK2/KIFW) (17)
IFWK2 = 1.0 - EXP(-KIFW) (18)
and
KIFW = -ALOG(IRC)*DELT60/24.0 (19)
where: IRC = interflow recession parameter (per day) DELT60 = number of hr per interval 24.0 = number of hours per day EXP = exponential function ALOG = natural logarithm function
IRC is the ratio of the present rate of interflow outflow to the value 24 hoursearlier, if there was no inflow. IRC can be input on a monthly basis to allow forvariation in soil properties throughout the year.
Module Section PWATER
68
4.2(1).3.4 Simulate Upper Zone Behavior (subroutine UZONE)
Purpose
This subroutine and the subsidiary subroutine UZONES are used to calculate thewater percolating from the upper zone. Water not percolated remains in upper zonestorage available for evapotranspiration in subroutine ETUZON.
Method of Determining Percolation
The upper zone inflow calculated in DISPOS is first added to the upper zone storageat the start of the interval to obtain the total water available for percolationfrom the upper zone.
Percolation only occurs when UZRAT minus LZRAT is greater than 0.01. When thishappens, percolation from the upper zone storage is calculated by the empiricalexpression:
PERC = 0.1*INFILT*INFFAC*UZSN*(UZRAT - LZRAT)**3 (20)
where: PERC = percolation from the upper zone (in/interval) INFILT = infiltration parameter (in/interval) INFFAC = factor to account for frozen ground, if any UZSN = parameter for upper zone nominal storage (inches) UZRAT = ratio of upper zone storage to UZSN LZRAT = ratio of lower zone storage to lower zone nominal storage (LZSN)
The upper zone nominal capacity can be input on a monthly basis to allow forvariations throughout the year. The monthly values are interpolated to obtain dailyvalues.
4.2(1).3.5 Simulate Lower Zone Behavior (subroutine LZONE)
Purpose
This subroutine determines the quantity of infiltrated and percolated water whichenters the lower zone. The infiltrated moisture supply is determined in subroutineDISPOS. The percolated moisture from the upper zone is found in subroutine UZONE.
Method
The fraction of the direct infiltration plus percolation that enters the lower zonestorage (LZS) is based on the lower zone storage ratio of LZS/LZSN where LZSN isthe lower zone nominal capacity. The inflowing fraction is determined empiricallyby:
Module Section PWATER
69
LZFRAC = 1.0 - LZRAT*(1.0/(1.0 + INDX))**INDX (21)
when LZRAT is less than 1.0, and by
LZFRAC = (1.0/(1.0 + INDX))**INDX (22)
when LZRAT is greater than 1.0. INDX is defined by:
INDX = 1.5*ABS(LZRAT - 1.0) + 1.0 (23)
where: LZFRAC = fraction of infiltration plus percolation entering LZS LZRAT = LZS/LZSN ABS = function for determining absolute value
These relationships are plotted in Figure 4.2(1).3-5. The fraction of the moisturesupply remaining after the surface, upper zone, and lower zone components aresubtracted is added to the groundwater storages.
4.2(1).3.6 Simulate Groundwater Behavior (subroutine GWATER)
Purpose
The purpose of this subroutine is to determine the amount of the inflow togroundwater that is lost to deep or inactive groundwater and to determine theamount of active groundwater outflow. These two fluxes will in turn affect theactive groundwater storage.
Method of Determining Groundwater Fluxes
The quantity of direct infiltration plus percolation from the upper zone which doesnot go to the lower zone (determined in subroutine LZONE) will be inflow to eitherinactive or active groundwater. The distribution to active and inactivegroundwater is user designated by parameter DEEPFR. DEEPFR is that fraction of thegroundwater inflow which goes to inactive groundwater. The remaining portion ofthe percolating water and all external lateral inflow if any make up the totalinflow to the active groundwater storage.
The outflow from active groundwater storage is based on a simplified model. Itassumes that the discharge of an aquifer is proportional to the product of thecross-sectional area and the energy gradient of the flow. Further, a representativecross-sectional area of flow is assumed to be related to the groundwater storagelevel at the start of the interval. The energy gradient is estimated as a basicgradient plus a variable gradient that depends on past active groundwateraccretion.
Module Section PWATER
70
Figure 4.2(1).3-5 Fraction of infiltration plus percolation entering lower zone storage
Module Section PWATER
71
Thus, the groundwater outflow is estimated by:
AGWO = KGW*(1.0 + KVARY*GWVS)*AGWS (24)
where: AGWO = active groundwater outflow (in/interval) KGW = groundwater outflow recession parameter (/interval) KVARY = parameter which can make active groundwater storage to outflow relation nonlinear (/inches) GWVS = index to groundwater slope (inches) AGWS = active groundwater storage at the start of the interval (inches)
GWVS is increased each interval by the inflow to active groundwater but is alsodecreased by 3 percent once a day. It is a measure of antecedent active ground-water inflow. KVARY is introduced to allow variable groundwater recession rates.When KVARY is nonzero, a semilog plot of discharge versus time is nonlinear. Thisparameter adds flexibility in groundwater outflow simulation which is useful insimulating many watersheds.
The parameter KGW is calculated by the Run Interpreter using the relationship:
KGW = 1.0 - (AGWRC)**(DELT60/24.0) (25) where: AGWRC = daily recession constant of groundwater flow if KVARY or GWVS = 0.0 That is, the ratio of current groundwater discharge to groundwater discharge 24-hr earlier DELT60 = hr/interval
4.2(1).3.7 Simulate Evapotranspiration (subroutine EVAPT)
Purpose
The purpose of EVAPT and its subordinate subroutines is to simulate evaporation andevapotranspiration fluxes from all zones of the pervious land segment. Since inmost hydrologic regimes the volume of water that leaves a watershed as evapotrans-piration exceeds the total volume of streamflow, this is an important aspect of thewater budget.
Method of Determining Actual Evapotranspiration
There are two separate issues involved in estimating evapotranspiration (ET).First, potential ET must be estimated. ET potential or demand is supplied as aninput times series, typically using U.S. Weather Bureau Class A pan records plusan adjustment factor. The data are further adjusted for cover in the parentsubroutine PWATER. Second, actual ET must be calculated, usually as a function ofmoisture storages and the potential. The actual ET is estimated by trying to meetthe demand from five sources in the order described below. The sum of the ET fromthese five sources is the total actual evapotranspiration from the land segment.
Module Section PWATER
72
Subroutine ETBASE
The first source from which ET can be taken is the active groundwater outflow orbaseflow. This simulates effects such as ET from riparian vegetation in whichgroundwater is withdrawn as it enters the stream. The user may specify by theparameter BASETP the fraction, if any, of the potential ET that can be sought fromthe baseflow. That portion can only be fulfilled if outflow exists. Any remainingpotential not met by actual baseflow evaporation will try next to be satisfied insubroutine EVICEP.
Subroutine EVICEP
Remaining potential ET exerts its demand on the water in interception storage.Unlike baseflow, there is no parameter regulating the rate of ET from interceptionstorage. The demand will draw upon all of the interception storage unless thedemand is less than the storage. When the demand is greater than the storage, theremaining demand will try to be satisfied in subroutine ETUZON.
Subroutine ETUZON
There are no special ET parameters for the upper zone, but rather ET is based onthe moisture in storage in relation to its nominal capacity. Actual evapotrans-piration will occur from the upper zone storage at the remaining potential demandif the ratio of UZS/UZSN, upper zone storage to nominal capacity, is greater than2.0. Otherwise the remaining potential ET demand on the upper zone storage isreduced; the adjusted value depends on UZS/UZSN. Subroutine ETAGW will attempt tosatisfy any remaining demand.
Subroutine ETAGW
Like ET from baseflow, actual evapotranspiration from active groundwater isregulated by a parameter. The parameter AGWETP is the fraction of the remainingpotential ET that can be sought from the active groundwater storage. That portionof the ET demand can be met only if there is enough active groundwater storage tosatisfy it. Any remaining potential will try to be met in subroutine ETLZON.
Subroutine ETLZON
The lower zone is the last storage from which ET is drawn. Evapotranspiration fromthe lower zone is more involved than that from the other storages. ET from thelower zone depends upon vegetation transpiration. Evapotranspiration opportunitywill vary with the vegetation type, the depth of rooting, density of the vegetationcover, and the stage of plant growth along with the moisture characteristics of thesoil zone. These influences on the ET opportunity are lumped into the LZETPparameter. Unlike the other ET parameters LZETP can be input on a monthly basisto account for temporal changes in the above characteristics.
Module Section PWATER
73
If the LZETP parameter is at its maximum value of one, representing near completeareal coverage of deep rooted vegetation, then the potential ET for the lower zoneis equal to the demand that remains. However, this is normally not the case.Usually vegetation type and/or rooting depths will vary over the land segment. Tosimulate this, a linear probability density function for ET opportunity is assumed(Figure 4.2(1).3-6). This approach is similar to that used to handle arealvariations in infiltration/percolation capacity.
The variable RPARM, the index to maximum ET opportunity, is estimated by:
RPARM = (0.25/(1.0 - LZETP))*(LZS/LZSN)*DELT60/24.0 (26) where: RPARM = maximum ET opportunity (in/interval) LZETP = lower zone ET parameter LZS = current lower zone storage (inches) LZSN = lower zone nominal storage parameter (inches) DELT60 = hr/interval
The quantity of water lost by ET from the lower zone storage, when remainingpotential ET (REMPET) is less than RPARM, is given by the cross-hatched area ofFigure 4.2(1).3-6. When REMPET is more than RPARM the lower zone ET is equal tothe entire area under the triangle, RPARM/2.
ET from the lower zone storage is further reduced when LZETP is less than 0.5 bymultiplying by LZETP*2.0. This is designed to account for the fraction of the landsegment devoid of any vegetation that can draw from the lower zone.
Module Section PWATER
74
Figure 4.2(1).3-6 Potential and actual evapotranspiration from the lower zone
Module Section SEDMNT
75
4.2(1).4 Simulate Production and Removal of Sediment (Section SEDMNT of Module PERLND)
Purpose
Module section SEDMNT simulates the production and removal of sediment from apervious land segment. Sediment can be considered to be inorganic, organic, orboth; the definition is up to the user.
Sediment from the land surface is one of the most common pollutants of waters fromurban, agricultural, and forested lands. It muddies waters, covers fish eggs, andlimits the capacity of reservoirs. Nutrients and toxic chemicals are carried by it.
Approach
The equations used to produce and remove sediment are based on the predecessormodels ARM and NPS (Donigian and Crawford, 1976 a,b). The algorithms representingland surface erosion in these models were derived from a sediment model developedby Moshe Negev (1967) and influenced by Meyer and Wischmeier (1969) and Onstad andFoster (1975). The supporting management practice factor which has been added tothe soil detachment by rainfall equation was based on the "P" factor in theUniversal Soil Loss Equation (Wischmeier and Smith, 1965). It was introduced inorder to better evaluate agricultural conservation practices. The equation thatrepresents scouring of the matrix soil (which was not included in ARM or NPS) wasderived from Negev's method for simulating gully erosion.
Figure 4.2(1).4-1 shows the detachment, attachment, and removal involved in theerosion processes on the pervious land surface, while Figure 4.2(1).4-2 schemat-ically represents the fluxes and storages used to simulate these processes. Twoof the sediment fluxes, SLSED and NSVI, are added directly to the detached sedimentstorage variable DETS in the parent routine SEDMNT while the other fluxes arecomputed in subordinate routines. SLSED represents external lateral input from anupslope land segment. It is a time series which the user may optionally specify.NVSI is a parameter that represents any net external additions or removals ofsediment caused by human activities or wind.
Removal of sediment by water is simulated as washoff of detached sediment instorage (WSSD) and scour of matrix soil (SCRSD). The washoff process involves twoparts: the detachment/attachment of sediment from/to the soil matrix and thetransport of this sediment. Detachment (DET) occurs by rainfall. Attachmentoccurs only on days without rainfall; the rate of attachment is specified byparameter AFFIX. Transport of detached sediment is by overland flow. The scouringof the matrix soil includes both pick-up and transport by overland flow combinedinto one process.
Module Section SEDMNT
76
Figure 4.2(1).4-1 Erosion processes
Module Section SEDMNT
77
Figure 4.2(1).4-2 Flow diagram for SEDMNT section of PERLND Application Module
Module Section SEDMNT
78
Module section SEDMNT has two options for simulating washoff of detached sedimentand scour of soil. One uses subroutine SOSED1 which is identical to the methodused in the ARM and the NPS Models. However, some equations used in this methodare dimensionally nonhomogeneous, and were designed for 15- and 5-min intervals.The results obtained are probably highly dependent on the simulation time step.The other option uses subroutine SOSED2, which is dimensionally homogeneous and is,generally less dependent on the time step.
4.2(1).4.1 Detach Soil By Rainfall (subroutine DETACH)
Purpose
The purpose of DETACH is to simulate the splash detachment of the soil matrixcaused by impact of rain.
Method of Detaching Soil by Rainfall
Kinetic energy from rain falling on the soil detaches particles which are thenavailable to be transported by overland flow. The equation that simulatesdetachment is:
DET = DELT60*(1.0 - CR)*SMPF*KRER*(RAIN/DELT60)**JRER (1)
where: DET = sediment detached from the soil matrix by rainfall (tons/ac/interval) DELT60 = number of hours/interval CR = fraction of the land covered by snow and other cover SMPF = supporting management practice factor KRER = detachment coefficient dependent on soil properties RAIN = rainfall (in/interval) JRER = detachment exponent dependent on soil properties
The variable CR is the sum of the fraction of the area covered by the snowpack(SNOCOV), if any, and the fraction that is covered by anything else but snow(COVER). SNOCOV is computed by section SNOW. COVER is a parameter which forpervious areas will typically be the fraction of the area covered by vegetation andmulch. It can be input on a monthly basis.
4.2(1).4.2 Remove by Surface Flow Using Method 1 (subroutine SOSED1)
Purpose
Subroutines SOSED1 and SOSED2 perform the same task but by different methods. Theysimulate the washoff of the detached sediment and the scouring of the soil matrix.
Module Section SEDMNT
79
Method
When simulating the washoff of detached sediment, the transport capacity of theoverland flow is estimated and compared to the amount of detached sedimentavailable. The transport capacity is calculated by the equation:
STCAP = DELT60*KSER*((SURS + SURO)/DELT60)**JSER (2)
where: STCAP = capacity for removing detached sediment (tons/ac/interval) DELT60 = hr/interval KSER = coefficient for transport of detached sediment SURS = surface water storage (inches) SURO = surface outflow of water (in/interval) JSER = exponent for transport of detached sediment
When STCAP is greater than the amount of detached sediment in storage, washoff iscalculated by:
WSSD = DETS*SURO/(SURS + SURO) (3)
If the storage is sufficient to fulfill the transport capacity, thenthe following relationship is used:
WSSD = STCAP*SURO/(SURS + SURO) (4)
where: WSSD = washoff of detached sediment (tons/ac/interval) DETS = detached sediment storage (tons/ac)
WSSD is then subtracted from DETS.
Transport and detachment of soil particles from the soil matrix is simulated withthe following equation:
SCRSD = SURO/(SURS + SURO)*DELT60*KGER*((SURS + SURO)/DELT6O)**JGER (5)
where: SCRSD = scour of matrix soil (tons/ac/interval) KGER = coefficient for scour of the matrix soil JGER = exponent for scour of the matrix soil
The sum of the two fluxes, WSSD and SCRSD, represents the total sediment outflowfrom the land segment.
Subroutine SOSED1 differs from SOSED2 in that it uses the dimensionallynonhomogeneous term (SURS + SURO)/DELT60 in the above equations, while SOSED2 usesthe homogeneous term SURO/DELT60.
Module Section SEDMNT
80
4.2(1).4.3 Remove by Surface Flow Using Method 2 (subroutine SOSED2)
Purpose
The purpose of this subroutine is the same as SOSED1. It only differs in method.
Method of Determining Removal
This method of determining sediment removal makes use of the dimensionallyhomogeneous term SURO/DELT60 instead of (SURO+SURS)/DELT60.
The capacity of the overland flow to transport detached sediment is determined inthis subroutine by:
STCAP = DELT60*KSER*(SURO/DELT60)**JSER (6)
When STCAP is more than the amount of detached sediment in storage, the flow washesoff all of the detached sediment storage (DETS). However, when STCAP is less thanthe amount of detached sediment in storage, the situation is transport limiting,so WSSD is equal to STCAP.
Direct detachment and transport of the soil matrix by scouring (e.g., gullying) issimulated with the equation:
SCRSD = DELT60*KGER*(SURO/DELT60)**JGER (7)
Definitions of the above terms can be found in subroutine SOSED2. The coefficientsand exponents will have different values than in subroutine SOSED1 because theymodify different variables.
4.2(1).4.4 Simulate Re-attachment of Detached Sediment (subroutine ATTACH)
Purpose
Subroutine ATTACH simulates the re-attachment of detached sediment (DETS) on thesurface (soil compaction).
Method
Attachment to the soil matrix is simulated by merely reducing DETS. Since the soilmatrix is considered to be unlimited, no addition to the soil matrix is necessarywhen this occurs. DETS is diminished at the start of each day that follows a daywith no precipitation by multiplying it by (1.0 - AFFIX), where AFFIX is aparameter. This represents a first-order rate of reduction of the detached soilstorage.
Module Section PSTEMP
81
4.2(1).5 Estimate Soil Temperatures (Section PSTEMP of Module PERLND)
Purpose
PSTEMP simulates soil temperatures for the surface, upper, and lower/groundwaterlayers of a land segment for use in module section PWTGAS and the agri-chemicalsections. Good estimates of soil temperatures are particularly important forsimulating first-order transformations in the agri-chemical sections.
Method
The two methods used for estimating soil temperatures are based on the regressionequation approach in the ARM Model (Donigian, et al., 1977) and the smoothingfactor approach used in HSP QUALITY (Hydrocomp, 1977) to simulate the temperaturesof subsurface flows.
Simulation of soil temperatures is done by layers which correspond to thosespecified in the agri-chemical sections. The surface layer is the portion of theland segment that affects overland flow water quality characteristics. Thesubsurface layers are upper, lower, and groundwater. The upper layer affectsinterflow quality characteristics while the lower layer is a transition zone togroundwater. The temperature of the groundwater layer affects groundwater qualitytransformations and outflow characteristics. Lower layer and groundwatertemperatures are considered approximately equal; a single value is estimated forboth layers.
Surface layer soil temperatures are estimated by the following regression equation:
SLTMP = ASLT + BSLT*AIRTC (1)
where: SLTMP = surface layer temperature (degrees C) ASLT = Y-intercept BSLT = slope AIRTC = air temperature (degrees C)
Temperatures of the other layers are simulated by one of two methods. If TSOPFGis set equal to 1 in the User's Control Input, the upper layer soil temperature isestimated by a regression equation as a function of air temperature (similar toequation above), and the lower layer/groundwater layer temperature is specified bya parameter which can vary monthly. This method is similar to that used in the ARMModel except that ARM relates the upper layer temperature to the computed soilsurface temperature instead of directly to air temperature.
If TSOPFG is set equal to zero, both the upper layer and the lower layer/groundwater layer temperatures are computed by using a mean departure from airtemperature plus a smoothing factor. The same basic equation is used with separatestate variables and parameters for each layer:
TMP = TMPS + SMO*(AIRTCS + TDIF - TMPS) (2)
Module Section PSTEMP
82
where: TMP = layer temperature at the end of the current interval (degrees C) SMO = smoothing factor (parameter) AIRTCS = air temperature at the start of the current interval (degrees C) TDIF = parameter which specifies the difference between the mean air temperature and the mean temperature of the soil layer (degrees C) TMPS = layer temperature at the start of the current interval (degrees C)
If TSOPFG is set to 2, then the method is identical to the above, except that thelower layer/groundwater layer temperature is estimated from the upper layertemperature (ULTMP), instead of directly from the air temperature.
The values of the parameters for any of the layer computations can be linearlyinterpolated from monthly input values to obtain daily variations throughout theyear. If this variation is not desired, the user may supply yearly (constant)values.
Module Section PWTGAS
83
4.2(1).6 Estimate Water Temperature and Dissolved Gas Concentrations (Section PWTGAS of Module PERLND)
Purpose
PWTGAS estimates water temperature and concentrations of dissolved oxygen andcarbon dioxide in surface, interflow, and groundwater outflows from a land segment.
Method
The temperature of each outflow is considered to be the same as the soiltemperature of the layer from which the flow originates, except that watertemperature can not be less than freezing. Soil temperatures must either becomputed in module section PSTEMP or supplied directly as an input time series.The temperature of the surface outflow is equal to the surface layer soiltemperature, the temperature of interflow to the upper layer soil temperature, andthe temperature of the active groundwater outflow equals the lower layer andgroundwater layer soil temperature.
The dissolved oxygen and carbon dioxide concentrations of the overland flow areassumed to be at saturation and are calculated as direct functions of watertemperature. PWTGAS uses the following empirical nonlinear equation to relatedissolved oxygen at saturation to water temperature (Committee on SanitaryEngineering Research, 1960):
SODOX = (14.652 + SOTMP*(-0.41022 + SOTMP*(0.007991 - 0.000077774*SOTMP)))*ELEVGC (1)
where: SODOX = concentration of dissolved oxygen in surface outflow (mg/l) SOTMP = surface outflow temperature (degrees C) ELEVGC = correction factor for elevation above sea level (ELEVGC is calculated by the Run Interpreter dependent upon mean elevation of the segment)
The empirical equation for dissolved carbon dioxide concentration of the overlandflow (Harnard and Davis, 1943) is:
SOCO2 = (10**(2385.73/ABSTMP - 14.0184 + 0.0152642*ABSTMP)) (2) *0.000316*ELEVGC*12000.0
where: SOCO2 = concentration of dissolved carbon dioxide in surface outflow (mg C/l) ABSTMP = temperature of surface outflow (degrees K)
The concentrations of dissolved oxygen and carbon dioxide in the interflow and theactive groundwater flow cannot be assumed to be at saturation. Values for theseconcentrations are provided by the user. They may be specified as constant valuesor 12 monthly values. If monthly values are provided, daily variation in valueswill automatically be obtained by linear interpolation between the monthly values.
Module Section PQUAL
84
4.2(1).7 Simulate Quality Constituents Using Simple Relationships with Sediment and Water Yield (Section PQUAL of Module PERLND)
Purpose
The PQUAL module section simulates water quality constituents or pollutants in theoutflows from a pervious land segment using simple relationships with water and/orsediment yield. Any constituent can be simulated by this module section. The usersupplies the name, units and parameter values appropriate to each of theconstituents that are needed in the simulation. However, more detailed methods ofsimulating sediment, heat, dissolved oxygen, dissolved carbon dioxide, nitrogen,phosphorus, soluble tracers, and pesticide removal from a pervious land segment areavailable, in other module sections.
Approach
The basic algorithms used to simulate quality constituents are a synthesis of thoseused in the NPS Model (Donigian and Crawford, 1976b) and HSP QUALITY (Hydrocomp,1977). However, some options and combinations are unique to HSPF.
Figure 4.2(1).7-1 shows schematically the fluxes and storages represented in modulesection PQUAL. The occurrence of a water quality constituent in both surface andsubsurface outflow can be simulated. The behavior of a constituent in surfaceoutflow is considered more complex and dynamic than the behavior in subsurfaceflow. A constituent on the surface can be affected greatly by adhesion to the soiland by temperature, light, wind, atmospheric deposition, and direct humaninfluences. Section PQUAL is able to represent these processes in a generalfashion. It allows quantities in the surface outflow to be simulated by twomethods. One approach is to simulate the constituent by association with sedimentremoval. The other approach is to simulate it using atmospheric deposition and/orbasic accumulation and depletion rates together with depletion by washoff; that is,constituent outflow from the surface is a function of the water flow and theconstituent in storage. A combination of the two methods may be used in which theindividual outfluxes are added to obtain the total surface outflow. Theseapproaches will be discussed further in the descriptions of the correspondingsubroutines. Concentrations of quality constituents in the subsurface flows ofinterflow and active groundwater are specified by the user. The concentration maybe linearly interpolated to obtain daily values from input monthly values.
The user has the option of simulating the constituents by any combination of thesesurface and subsurface outflow pathways. The outflux from the combination of thepathways simulated will be the total outflow from the land segment. In addition,the user is able to select the units to be associated with the fluxes. Theseoptions provide considerable flexibility. For example, a user may wish to simulatecoliforms in units of organisms/ac by association with sediment in the surfacerunoff and use a concentration in the groundwater which varies seasonally. Or auser may want to simulate total dissolved salts in pounds per acre by directassociation with overland flow and a constant concentration in interflow andgroundwater flow.
Module Section PQUAL
85
Figure 4.2(1).7-1 Flow diagram for PQUAL section of the PERLND Application Module
Module Section PQUAL
86
PQUAL allows the user to simulate up to 10 quality constituents at a time. Eachof the 10 constituents may be defined as one or a combination of the followingtypes: QUALSD, QUALOF, QUALIF, and/or QUALGW. If a constituent is considered tobe associated with sediment, it is called a QUALSD. The corresponding terms forconstituents associated with overland flow, interflow, and groundwater flow areQUALOF, QUALIF, and QUALGW, respectively. Note that only a QUALOF may receiveatmospheric deposition, since it is the only type to maintain a storage. However,no more than seven of any one of the constituent types (QUALSD, QUALOF, QUALIF, orQUALGW) may be simulated in one operation. The program uses a set of flag pointersto keep track of these associations. For example, QSDFP(3) = 0 means that thethird constituent is not associated with sediment, whereas QSDFP(6) = 4 means thatthe sixth constituent is the fourth sediment associated constituent (QUALSD).Similar flag pointer arrays are used to indicate whether or not a qualityconstituent is a QUALOF, QUALIF, or QUALGW.
4.2(1).7.1 Remove by Association with Sediment (subroutine QUALSD)
Purpose
QUALSD simulates the removal of a quality constituent from a pervious land surfaceby association with the sediment removal determined in module section SEDMNT.
Method
This approach assumes that the particular quality constituent removed from the landsurface is in proportion to the sediment removal. The relation is specified withuser-input "potency factors." Potency factors indicate the constituent strengthrelative to the sediment removed from the surface. Various quality constituentssuch as iron, lead, and strongly adsorbed toxicants are actually attached to thesediment being removed from the land surface. Some other pollutants such asammonia, organics, pathogens, and BOD may not be extensively adsorbed, but can beconsidered highly correlated to sediment yield.
For each quality constituent associated with sediment, the user supplies separatepotency factors for association with washed off and scoured sediment (WSSD andSCRSD). Typically, the washoff potency factor would be larger than the scourpotency factor because washed off sediment is usually finer than the scouredmaterial and thus has a higher adsorption capacity. Organic nitrogen would be acommon example of such a constituent. The user is also able to supply monthlypotency factors for constituents that vary somewhat consistently during the year.For instance, constituents that are associated with spring and fall fertilizationmay require such monthly input values.
Removal of the sediment associated constituent by detached sediment washoff issimulated by:
WASHQS = WSSD*POTFW (1)
Module Section PQUAL
87
where: WASHQS = flux of quality constituent associated with detached sediment washoff (quantity/ac per interval) WSSD = washoff of detached sediment (tons/ac per interval) POTFW = washoff potency factor (quantity/ton)
Removal of constituents by scouring of the soil matrix is similar:
SCRQS = SCRSD*POTFS (2)
where: SCRQS = flux of quality constituent associated with scouring of the matrix soil (quantity/ac per interval) SCRSD = scour of matrix soil (tons/ac per interval) POTFS = scour potency factor (quantity/ton)
WASHQS and SCRQS are combined to give the total sediment associated flux of theconstituent from the land segment, SOQS.
The unit "quantity" refers to mass units (pounds or tons in the English system) orsome other quantity, such as number of organisms for coliforms. The unit is user-specified.
4.2(1).7.2 Accumulate and Remove by a Constant Unit Rate and by Overland Flow (subroutine QUALOF)
Purpose
QUALOF simulates the accumulation of a quality constituent on the pervious landsurface and its removal by a constant unit rate and by overland flow.
Method
This subroutine differs from the others in module section PQUAL in that the storageof the quality constituent on the land surface is simulated. The constituent canbe accumulated and removed by processes which are independent of storm events suchas cleaning, decay, and wind erosion and deposition, or it can be washed off byoverland flow. The accumulation and removal rates can have monthly values toaccount for seasonal fluctuations. A pollution indicator such as fecal coliformfrom range land is an example of a constituent with accumulation and removal rateswhich may need to vary throughout the year. The concentration of the coliform inthe surface runoff may fluctuate with the seasonal grazing density, and theweather.
The constituent may, alternatively or additionally, receive atmospheric deposition.Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a time
Module Section PQUAL
88
series, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forPERLND, and are specified in the EXT SOURCES block of the UCI. The monthly valuesare input in the MONTH-DATA block in the UCI.
If atmospheric deposition data are input to the model, the storage of qual isupdated as follows:
SQO = SQO + ADFX + PREC*ADCN (3)
where: SQO = storage of available quality constituent on the surface (mass/area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation depth ADCN = concentration for wet atmospheric deposition (mass/volume)
When there is surface outflow and some quality constituent is in storage, washoffis simulated using the commonly used relationship:
SOQO = SQO*(1.0 - EXP(-SURO*WSFAC)) (4)
where: SOQO = washoff of the quality constituent from the land surface (quantity/ac per interval) SQO = storage of available quality constituent on the surface (quantity/ac) SURO = surface outflow of water (in/interval) WSFAC = susceptibility of the quality constituent to washoff (/in) EXP = exponential function
The storage is updated once a day to account for accumulation and removal whichoccurs independent of runoff by the equation:
SQO = ACQOP + SQOS*(1.0 - REMQOP) (5)
where: ACQOP = accumulation rate of the constituent (quantity/ac per day) SQOS = SQO at the start of the interval REMQOP = unit removal rate of the stored constituent (per day)
The Run Interpreter computes REMQOP and WSFAC for this subroutine according to: REMQOP = ACQOP/SQOLIM (6)
where: SQOLIM = asymptotic limit for SQO as time approaches infinity (quantity/ac), if no washoff occurs
and
WSFAC = 2.30/WSQOP (7)
Module Section PQUAL
89
where: WSQOP = rate of surface runoff that results in 90 percent washoff in one hour (in/hr)
Since the unit removal rate of the stored constituent (REMQOP) is computed from twoother parameters, it does not have to be supplied by the user.
4.2(1).7.3 Simulate by Association with Interflow Outflow (subroutine QUALIF)
Purpose
QUALIF is designed to permit the user to simulate the occurrence of a constituentin interflow.
Method
The user specifies a concentration for each constituent which is a QUALIF.Optionally, one can specify 12 monthly values, to account for seasonal variation.In this case, the system interpolates a new value each day.
4.2(1).7.4 Simulate by Association with Active Groundwater Outflow (subroutine QUALGW)
Purpose
QUALGW is designed to permit the user to simulate the occurrence of a constituentin groundwater outflow.
Method
The method is identical to that for QUALIF.
Introduction to the Agri-Chemical Sections
90
Introduction to the Agri-chemical Sections
The introduction of agricultural chemicals into streams, lakes, and groundwaterfrom agricultural land may be detrimental. For example, persistent fat solublepesticides, such as DDT, have been known to concentrate in the fatty tissue ofanimals causing toxic effects. Nitrogen and phosphorus are essential plantnutrients which when introduced into certain surface waters will increaseproductivity. This may or may not be desirable depending upon managementobjectives. Significant productivity results in algal blooms, but some increasein productivity will increase fish production. Drinking water containing highnitrate concentrations may cause methomoglobinemia in small children.
Pesticide, nitrogen, and phosphorus compounds are important to agriculturalproduction, but prediction of their removal from the field is necessary for wisemanagement of both land and water resources. HSPF can be used to predict suchoutflows. The agri-chemical sections of the PERLND module of HSPF simulate indetail nutrient and pesticide processes, both biological and chemical, and themovement of any nonreactive tracer in a land segment. These chemicals can also besimulated in module section PQUAL, but in a simplified manner. The dynamic andcontinuous processes that affect the storages and outflow of pesticides and ofnutrients from fertilized fields should be simulated in detail to fully analyzeagricultural runoff. If the situation does not require full representation ofthese processes, or if data are not available, the PQUAL subroutines could be used.
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog for modulePERLND, and are specified in the EXT SOURCES block of the UCI. The monthly valuesare input in the MONTH-DATA block in the UCI.
An additional use of the atmospheric deposition time series is specifying inputsof agricultural chemicals and fertilizers instead of changing soil storages in theSPEC-ACTIONS block. For this purpose, deposition to the upper soil layer inaddition to the surface soil layer has been provided for in sections NITR, PHOS,and TRACER. (Section PEST has time series only for the surface layer, sincepesticides are not normally incorporated into the soil as are fertilizers.)Depending on the complexity of the agricultural practices being modeled, the usershould decide whether the SPEC-ACTIONS block or the time series is simpler toconstruct.
The basic algorithms in the agri-chemical sections of HSPF were originallydeveloped for use on agricultural lands, but can be used on other pervious areaswhere pesticides and plant nutrients occur, for example, orchards, nursery land,parks, golf courses, and forests. All pervious land contains nitrogen andphosphorus in the soil; it is possible to use this module to simulate the behaviorof agricultural chemicals in any such area.
Introduction to the Agri-Chemical Sections
91
Comparison of HSPF and ARM
The methods used to simulate pesticide processes in the agri-chemical sections weredeveloped originally for the Pesticide Transport and Runoff (PTR) Model (Crawfordand Donigian, 1973), then expanded to include nutrients in the Agricultural RunoffManagement (ARM) Model (Donigian and Crawford, 1976) and tested and modified in ARMVersion II (Donigian, et al., 1977). In HSPF the ARM Version II algorithms wererecreated with some additional options. (For more detail on the basic methods,refer to the above reports.)
The differences between HSPF and ARM Model Version II should, however, bediscussed. The biggest difference is the availability of new options to simulatesoil nutrient and pesticide adsorption and desorption. Ammonium and phosphateadsorption/desorption in HSPF can be accomplished by using Freundlich isotherms aswell as by first-order kinetics. Pesticides can be adsorbed and desorbed by thetwo Freundlich methods used in the ARM Model or by first-order kinetics. Inaddition, the pesticide parameter values are now input for each separate soil layerinstead of inputting one parameter set for all the layers. HSPF also allows theuser to simulate more than one pesticide in a run. (The ARM Model only simulatesone per run). In addition to the percolation factors which can still be used toretard any solute leaching from the upper layer and lower layer, a multiplicationfactor has been introduced that can reduce leaching from the surface layer. Also,in HSPF, nitrogen and phosphorus chemical and biochemical transformations can eachbe simulated at different time steps to save computer time. Plant uptake ofammonium is also available in HSPF.
Units
The fluxes and storages of chemicals modeled in these module sections are in massper area units. The user must supply the input in appropriate units; kg/ha for theMetric system, and lb/ac for the English system. Internally, most of the code doesnot differentiate between the unit systems. Fluxes are determined by eitherproportionality constants, fractions of chemicals in storage, or unitlessconcentrations. First-order kinetics makes use of proportionality constants fordetermining reaction fluxes. Chemicals are transported based on the fractions ofthat compound in storage. Freundlich adsorption/desorption is based on ppmconcentrations.
Module Sections
There are five agri-chemical module sections. They are shown in the structurechart of PERLND (Figure 4.2(1)-1). Module section MSTLAY manipulates waterstorages and fluxes calculated in module section PWATER. This section must be runbefore the following sections can be run, since it supplies them with data forsimulating the storage and movement of solutes. Module section PEST simulatespesticide behavior while NITR and PHOS simulate the plant nutrients of nitrogen andphosphorus. Simulation of a nonreactive solute is accomplished in module sectionTRACER.
Module Section MSTLAY
92
4.2(1).8 Estimate Moisture Content of Soil Layers and Fractional Fluxes (Section MSTLAY of Module PERLND)
Purpose
This module section estimates the storages of moisture in the four soil layers withwhich the agricultural chemical sections deal (Figure 4.2(1).8-1); and the fluxesof moisture between the storages. MSTLAY is required because the moisture storagesand fluxes computed by module section PWATER can not be directly used to simulatesolute transport through the soil. For example, in PWATER, some moisture whichinfiltrates can reach the groundwater in a single time step (Figure 4.2(1).3-2).While this phenomenon does not have any serious effect in simulating the hydrologicresponse of a land segment, it does seriously affect the simulation of solutetransport.
Thus, MSTLAY takes the fluxes and storages computed in PWATER and adapts them tofit the storage/flow path picture in Figure 4.2(1).8-1. The revised storages, ininches of water, are also expressed in mass/area units (that is, lb/ac or kg/ha)for use in the adsorption/desorption calculations.
Method
Figure 4.2(1).8-1 schematically diagrams the moisture storages and fluxes used insubroutine MSTLAY. Note that the fluxes are represented in terms of both quantity(e.g., IFWI, in inches/interval) and as a fraction of the contributing storage(e.g., FII, as a fraction of UMST/interval). The reader should also refer toFigure 4.2(1).3-2 in module section PWATER when studying this diagram and thefollowing discussion.
For the agri-chemical sections the moisture storages (the variables in Figure4.2(1).8-1 ending in MST) are calculated by the general equation:
MST = WSTOR + WFLUX (1)
The variable WSTOR is the related storage calculated in module section PWATER(Figure 4.2(1).3-2). For example, in the calculation of the lower layer moisturestorage (LMST), WSTOR is the lower zone storage (LZS). The variable WFLUXgenerally corresponds to the flux of moisture through the soil layer. For thecomputation of LMST, WFLUX is the sum of water percolating from the lower zone tothe inactive (IGWI) and active groundwater (AGWI) as determined in section PWATER.Note that these equations are dimensionally non-homogeneous, because storages(inches) and fluxes (inches/interval) are added together. Thus, the results givenare likely to be highly dependent on the simulation time step. The ARM Model, fromwhich the equations come, uses a time step of 5 minutes. Caution should beexercised if the agricultural chemical sections (including MSTLAY) are run with anyother time step.
The upper layer has been subdivided into two storages, principal and transitory.The transitory (interflow) storage is used to transport chemicals from the upperlayer to interflow outflow. The chemicals in it do not undergo any reactions.However, reactions do occur in the principal storage.
Module Section MSTLAY
93
Figure 4.2(1).8-1 Flow diagram showing transport of moisture and solutes as estimated in the MSTLAY section of the PERLND Application Module
Module Section MSTLAY
94
The fluxes shown in Figure 4.2(1).8-1 are the same as those in Figure 4.2(1).3-2with the exceptions of SDOWN and UDOWN. SDOWN encompasses all the water that movesdownward from the surface layer storage. It is the combination of the waterinfiltrating from the surface detention storage directly to the lower zone (INFIL),the inflow to the upper zone (UZI), and the water flowing into interflow storage(IFWI). UDOWN is all the water percolating through the upper layer. It is INFILplus the percolation from the upper zone storage to the lower zone storage (PERC).
Each fractional solute flux is the appropriate moisture flux divided by thecontributing storage. For example, the fraction of chemical in solution that istransported overland from the surface layer storage (FSO) is the surface moistureoutflow (SURO) divided by the surface layer moisture storage (SMST).
The above estimates are based on the assumption that the concentration of thesolute being transported is the same as that in storage. They also assume uniformflow through the layers and continuous mixing of the solutes. However, theseassumptions may need to be revised or implemented differently for some of thetransport. Past testing has shown that the above method leads to excessiveleaching of solutes (Donigian, et al., 1977). Factors that retard solute leachingwere added in the ARM Model Version II to remedy this problem. For the surfacelayer, the percolation factor (SLMPF) affects the solute fraction percolating (FSP)by the relationship:
FSP = SLMPF*SDOWN/SMST (2)
The variables SDOWN and SMST are defined in Figure 4.2(1).8-1. FSP will typicallybe between 0 and 1.
For the upper or lower layer percolating fraction (FUP, FLDP, or FLP), theretardation factor only has an influence when the ratio of the respective zonestorage to the nominal storage times the factor (ZS/(ZSN*LPF)) is less than one.The relationship under this condition is:
F = (ZS/(ZSN*LPF))*(PFLUX/MST) (3)
where: F = layer solute percolating fraction ZS = zone moisture storage, either UZS or LZS ZSN = zone nominal moisture storage, either UZSN or LZSN LPF = factor which retards solute leaching for the layer, either ULPF or LLPF PFLUX = percolation flux, either UDOWN, IGWI, or AGWI MST = layer moisture storage, either UMST or LMST
Module Section PEST
95
4.2(1).9 Simulate Pesticide Behavior in Detail (Section PEST of Module PERLND)
Purpose
Because of the complexity of pesticide behavior on the land, simulation of theprocesses frequently requires considerable detail. Pesticide applications vary inamount and time during the year. Various pesticides adsorb and degradedifferently. Some, like paraquat, attach themselves strongly to the soil, therebyappearing in low concentrations in water but in high concentrations on soilparticles. Others, like atrazine, undergo complex interactions with the soil andare found in higher concentrations in the runoff water than on the eroded sediment.
Section PEST models pesticide behavior by simulating the processes of degradationand adsorption as well as transport. The pesticides are simulated in the soil andrunoff in three forms: dissolved, adsorbed, and crystallized. These phases in thesoil affect the forms and amounts in the runoff.
Method
Pesticides are simulated by using the time series generated by other PERLND modulesections to transport and influence the adsorption and degradation processes.Pesticides move with water flow or by association with the sediment. They also maybe adsorbed to the soil in varying degree as a function of the chemical character-istics of the toxicant and the exchange capacity of the soil layer. Pesticidedegradation occurs to varying degrees depending upon the susceptibility of thecompound to volatilization and breakdown by light, heat, microorganisms andchemical processes. The subroutines in module section PEST consider thesetransport and reaction processes.
All the subroutines described in this module section except NONSV and DEGRAS areaccessed by other agri-chemical module sections because many of the basic transportand reaction processes are similar. The subroutines are described here becausethey are physically located in this subroutine group. Subroutine AGRGET is firstto be called. This subroutine has no computing function; it obtains any requiredtime series (that is not already available) from the INPAD.
Inputs of pesticide to the surface soil layer may be simulated using either or bothof two methods: 1) as changes to storage variables in the SPEC-ACTIONS block or 2)as atmospheric deposition. However, atmospheric deposition affects only thesurface layer, so if a pesticide is incorporated into the upper soil layer, specialactions must be used.
Atmospheric deposition inputs of pesticide can be specified in two possible waysdepending on the form of the available data. If the deposition is in the form ofa flux (mass per area per time), then it is considered "dry deposition". If thedeposition is in the form of a concentration in rainfall, then it is considered"wet deposition", and the program automatically combines it with the input rainfalltime series to compute the resulting flux. Either type of deposition data can beinput as a time series, which covers the entire simulation period, or as a set of
Module Section PEST
96
monthly values that is used for each year of the simulation. The specificpesticide atmospheric deposition time series are documented in the EXTNL table ofthe Time Series Catalog for PERLND, and are specified in the EXT SOURCES block ofthe UCI. The monthly values are input in the MONTH-DATA block in the UCI.
If atmospheric deposition data are input to the model, the surface storage isupdated for each of the three forms of each pesticide using the formula:
SPS = SPS + ADFX + PREC*ADCN (1)
where: SPS = storage of pesticide form on the surface (mass/area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation depth ADCN = concentration for wet atmospheric deposition (mass/volume)
Subroutine SDFRAC determines the fraction of the surface layer soil that haseroded. The amount eroded is the total sediment removed by scour and washoff asdetermined in module section SEDMNT. The mass of soil in the surface layer is aparameter value which does not vary even when material is removed. The chemicalwhich is associated with the sediment is assumed to be removed from the surfacelayer storage in the same proportion that the layer has eroded. Chemical removalis simulated in subroutine SEDMOV. A sediment associated chemical is one that maybe attached to the eroding soil or one which may move with the soil. Withpesticides the adsorbed form will be attached to the soil particle, while thecrystalline form will move with the soil particle being eroded but will not beattached to it. Both forms are taken from their respective surface layer storagesin proportion to the fraction of the surface soil layer removed by overland flow.
Subroutines TOPMOV and SUBMOV perform a function similar to SEDMOV except they movethe solutes. Chemicals in solution move to and from the storages according to thefractions calculated in section MSTLAY. Figure 4.2(1).9-1 schematicallyillustrates the fluxes and storages used in these subroutines. The fractions(variables beginning with the letter "F") of the storages are used to compute thesolute fluxes. The equations used to compute the solute transport fluxes from thefractions and storages are given in the figure. Subroutine TOPMOV performs thecalculations of the fluxes and the resulting changes in storage for the topsoillayers (surface and upper), while SUBMOV performs them for the subsurface layers(lower and active groundwater).
Biological and chemical reactions are performed on the pesticides (and otherchemicals) in each layer storage. Chemicals in the upper layer principal storageundergo reactions while those in the transitory (interflow) storage do not. Theupper layer transitory storage is a temporary storage of chemicals on their way tointerflow outflow. Subroutine PSTRXN is called to perform reactions on thepesticide in each layer.
Module Section PEST
97
Figure 4.2(1).9-1 Flow diagram showing modeled movement of chemicals in solution
Module Section PEST
98
4.2(1).9.5 Perform Reactions on Pesticides (subroutine PSTRXN)
Purpose
This code simulates the degradation and adsorption/desorption of pesticides. Thissubroutine is called for each of the four soil layers and each pesticide.
Method of Reacting Pesticides
The user has the option of adsorbing/desorbing the pesticide by one of threemethods. The first method is by first-order kinetics. This method assumes thatthe pesticide adsorbs and desorbs at a rate based on the amount in soil solutionand on the amount on the soil particle. It makes use of a proportionality constantand is independent of the concentration. The second method is by use of the singlevalue Freundlich isotherm. This method makes use of a single adsorption/desorptioncurve for determining the concentration on the soil and in solution. The thirdmethod is by use of multiple curves based on a varying Freundlich K value. Furtherdetails of these methods can be found in the discussion of the individualsubroutines that follows and in documentation of the ARM Model (Donigian andCrawford, 1976; Donigian, et al., 1977).
Degradation is performed once a day in each of the four layers that containpesticide. The amount degraded is determined simply by multiplying a decay rateparameter specified for each soil layer by each of the three forms (adsorbed,solution, and crystalline) of pesticide in storage. The degraded amounts are thensubtracted from their respective storages. This method of simulating degradationlumps complex processes in a simple parameter.
Module Section PEST
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4.2(1).9.5.1 Adsorb/Desorb Using First-Order Kinetics (subroutine FIRORD)
Purpose
The purpose of this subroutine is to calculate the adsorption and desorptionreaction fluxes of chemicals using temperature dependent first-order kinetics.These fluxes are calculated every simulation interval when the subroutine is calledby section PEST, but they are determined only at the designated chemical reactionfrequency when called by sections NITR and PHOS.
Method
The calculation of adsorption and desorption reaction fluxes by first-orderkinetics for soil layer temperatures less than 35 degrees C takes the form:
DES = CMAD*KDS*THKDS**(TMP-35.0) (1)
ADS = CMSU*KAD*THKAD**(TMP-35.0) (2)
where: DES = current desorption flux of chemical (mass/area per interval) CMAD = storage of adsorbed chemical (mass/area) KDS = first-order desorption rate parameter (per interval) THKDS = temperature correction parameter for desorption TMP = soil layer temperature (degrees C) ADS = current adsorption flux of chemical (mass/area per interval) CMSU = storage of chemical in solution (mass/area) KAD = first-order adsorption rate parameter (per interval) THKAD = temperature correction parameter for adsorption THKDS and THKAD are typically about 1.06
All of the variables except the temperature coefficients may vary with the layerof the soil being simulated. The soil temperatures are time series which may beinput (e.g., using field data) or simulated in module section PSTEMP. Thetemperature correction of the reaction rate parameter is based on the Arrheniusequation. At temperatures of 35 degrees C or above, no correction is made. Whenthe temperature is at 0 degrees C or below or the soil layer is dry, no adsorptionand desorption occurs.
The storage of the solution chemical is updated every simulation interval in thecalling subroutine, that is, in PSTRXN, NITRXN, or PHORXN, by adding DES minus ADS.Likewise, the storage of the adsorbed chemical is updated there also by adding ADSminus DES. An adjustment is made in the calling subroutine if any of the fluxeswould cause a storage to go negative. When this happens a warning message isproduced and fluxes are adjusted so that no storage goes negative. This usuallyoccurs when large time steps are used in conjunction with large KAD and KDS values.
Module Section PEST
100
4.2(1).9.5.2 Adsorb/Desorb Using the Single Value Freundlich Method (subroutine SV)
Purpose
Subroutine SV calculates the adsorption and desorption and the resulting newstorages of a chemical using the single value Freundlich method.
Method
The Freundlich isotherm methods, unlike first-order kinetics, assume instantaneousequilibrium. That is, no matter how much chemical is added to a particular phase,equilibrium is assumed to be established between the solution and adsorbed phaseof the chemical. These methods also assume that for any given amount of chemicalin the soil, the equilibrium distribution of the chemical between the soil solutionand on the soil particle can be found from an isotherm. Figure 4.2(1).9-2illustrates such an isotherm.
Three phases of the chemical are actually possible; crystalline, adsorbed, andsolution. The crystalline form is assumed to occur only when the soil layer isdry, or when there is more chemical in the layer than the combined capacity toadsorb and hold in solution. When the soil is dry, all the chemical is consideredto be crystalline salt. When there is more total chemical in the soil layer thanthe soil adsorption sites can contain and more than that saturated in solution,then the chemical content which exceeds these capacities is considered to becrystalline salt. Module section PEST considers crystalline phase storage, butmodule sections NITR and PHOS do not. Instead, any crystalline phosphate orammonium predicted by an isotherm is added to the adsorbed phase storage.
The adsorbed and solution phases of the chemical are determined in this subroutineby the standard Freundlich equation as plotted by curve 1 in Figure 4.2(1).9-2.When the amount of chemical is less than the capacity of the soil particle latticeto permanently bind the chemical (XFIX), then all the material is consider fixed.All the fixed chemical is contained in the adsorbed phase of the layer storage.Otherwise, the Freundlich equation for curve 1 is used to determine thepartitioning of the chemical into the adsorbed and solution phases:
X = KF1*C**(1/N1) + XFIX (3) where: X = chemical adsorbed on soil (ppm of soil) KF1 = single value Freundlich K coefficient C = equilibrium chemical concentration in solution (ppm of solution) N1 = single value Freundlich exponent XFIX = chemical which is permanently fixed (ppm of soil)
The above equation is solved in subroutine ITER by an iteration technique. Theparameters used in the computation can differ for each layer of the soil.
Module Section PEST
101
Figure 4.2(1).9-2 Freundlich isotherm calculations
Module Section PEST
102
4.2(1).9.5.3 Adsorb/Desorb Using the Non-single Value Freundlich Method (subroutine NONSV)
Purpose
The purpose of this subroutine is to calculate the adsorption/desorption of achemical by the nonsingle value Freundlich method. The single value Freundlichmethod was found to inadequately represent the division of some pesticides betweenthe soil particle and solution phases, so this method was developed as an optionin the ARM Model (Donigian and Crawford, 1976). This routine is only available foruse by the PEST module section.
Method
The approach in this code uses the same algorithms and solution technique assubroutine SV for determining curve 1 in Figure 4.2(1).9-2. However, curve 1 isused solely for adsorption. That is, only when the concentration of the adsorbedchemical is increasing. When desorption occurs a new curve (curve 2) is used:
X = KF2*C**(1/N2) + XFIX (4)
KF2 = (KF1/XDIF)**(N1/N2) * XDIF (5)
where: KF2 = nonsingle value Freundlich coefficient N2 = nonsingle value Freundlich exponent parameter XDIF = XJCT - XFIX XJCT = the adsorbed concentration where curve 1 joins curve 2 (i.e., where desorption started) as shown in Figure 4.2(1).9-2 (ppm of soil)
The other variables are as defined for subroutine SV.
Once curve 2 is used, both desorption and adsorption follow it until the adsorbedconcentration is less than or equal to XFIX or until it reaches XJCT. Then,adsorption will again take place following curve 1 until desorption reoccurs,following a newly calculated curve 2. The solution of the Freundlich equations forcurves 1 and 2 utilizes the same iteration technique introduced in subroutine SV(subroutine ITER).
Module Section NITR
103
4.2(1).10 Simulate Nitrogen Behavior in Detail (section NITR of module PERLND)
Purpose
NITR, like section PEST, simulates the behavior of chemicals in the soil profileof a land segment. Section NITR handles the nitrogen species of nitrate, ammonia,and organic nitrogen. This involves simulating nitrogen transport and soilreactions. Nitrogen, like phosphorus, may be a limiting nutrient in the eutrophic-ation process in lakes and streams. Nitrates in high concentrations may also posea health hazard to infants.
Method
Nitrogen species are transported by the same methods used for the pesticide forms.The subroutines that are called to transport nitrogen are located in and describedwith the PEST module section. Adsorbed ammonium and two forms of particulateorganic nitrogen (labile and refractory) are removed from the surface layer storageby association with sediment. These processes are handled by subroutines SDFRACand SEDMOV. Nitrate, ammonium, and two forms of dissolved organic nitrogen (labileand refractory) in the soil water are transported using the subroutines TOPMOV andSUBMOV. Nitrogen reactions are simulated separately for each of the soil layers.The methods are described below, in the discussion of subroutine NITRXN, and areshown schematically in Figure 4.2(1).10-1.
Inputs of nitrogen to the soil
Natural and agricultural inputs of nitrogen to the surface and upper soil layerscan be simulated using either or both of two methods: 1) as changes to storagevariables in the SPEC-ACTIONS block, or 2) as atmospheric deposition inputs.Atmospheric deposition inputs are implemented for three species: nitrate, ammonium,and particulate labile organic nitrogen.
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forPERLND, and are specified in the EXT SOURCES block of the UCI. The monthly valuesare input in the MONTH-DATA block in the UCI.
If atmospheric deposition data are input to the model, the soil storage is updatedfor each of the three species of nitrogen in each soil layer (surface and upper)using the general formula:
NSTOR = NSTOR + ADFX + PREC*ADCN (1)
Module Section NITR
104
Figure 4.2(1).10-1 Flow diagram for nitrogen reactions
Module Section NITR
105
where: NSTOR = storage of nitrogen species in the soil layer (mass/area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation (depth per interval) ADCN = concentration of nitrogen species in rainfall (mass/volume)4.2(1).10.1
Perform Reactions on Nitrogen Forms (subroutine NITRXN)
Purpose
The purpose of NITRXN is to simulate soil nitrogen transformations. This includesplant uptake of nitrate and ammonium, return of plant nitrogen to organic nitrogen,denitrification or reduction of nitrate-nitrite, immobilization of nitrate-nitriteand ammonium, mineralization of organic nitrogen, fixation of atmospheric nitrogen,volatilization of ammonium, adsorption/desorption of ammonium, and partitioning oftwo types of organic nitrogen between solution and particulate forms.
Method of Nitrogen Transformations
Adsorption/desorption of ammonium
Nitrogen reactions can be divided between those that are chemical in nature andthose that are a combination of chemical and biological reactions. The adsorptionand desorption of ammonium is a chemical process. The user has the option ofsimulating ammonium adsorption and desorption by first-order kinetics withsubroutine FIRORD or by the Freundlich isotherm method with subroutine SV. Thesesubroutines are described in the PEST module section.
The user has the option of specifying how often the adsorption and desorption ratesare calculated. When adsorption/desorption is simulated by the Freundlich method,the solution and adsorbed storages of ammonium are determined instantaneously atthe specified frequency of reaction. However, when the first-order method is used,the temperature-corrected reaction fluxes (Figure 4.2(1).10-1) are recomputedintermittently, but the storages are updated every simulation interval.
Organic Nitrogen Partitioning
Organic nitrogen is assumed to exist in the following four forms in each soillayer.
Particulate labileSolution labileParticulate refractorySolution refractory
The particulate labile species is the default form of organic N in the model; ifdefault values of organic N-related sorption parameters and reaction rates areused, only this form will exist. This species is formed by immobilization ofnitrate and ammonia, and is converted back to ammonia by mineralization in the
Module Section NITR
106
soil. It also is transported on the surface by association with sediment.However, if the user inputs non-zero values of the parameters, it can undergoconversion by first-order rate to the particulate refractory form, and it can alsodesorb to the solution labile form. The particulate refractory species can alsodesorb to the solution refractory form. The two solution species are available fortransport with surface runoff and within the soil profile, and the particulaterefractory form can be transported on the surface with sediment.
The organic nitrogen partitioning reactions (sorption-desorption) are described byequilibrium isotherms as shown in the following equations:
KLON = PLON/SLON (2) KRON = PRON/SRON
where: KLON and KRON = partition coefficients for the labile and refractory organic nitrogen, respectively (-) PLON = particulate labile organic N (lb N/ac or kg N/ha) SLON = solution labile organic N (lb N/ac or kg N/ha) PRON = particulate refractory organic N (lb N/ac or kg N/ha) SRON = solution refractory organic N (lb N/ac or kg N/ha)
The four organic nitrogen forms and their assorted reactions are illustrated inFigure 4.2(1).10-1. Note that the storages and transformations in this figure aregenerally repeated in each soil layer except for the aboveground plant N and thelitter compartments.
Note that the three new organic nitrogen state variables described above are alwayspresent in the NITR module, i.e., they are not optional. However, in order to makethe program compatible with previous versions, the default inputs result in thethree new forms maintaining zero concentrations; therefore, there should be noimpact on the simulation results of existing applications when using the currentversion of HSPF.
Other Nitrogen Transformations
The other reactions are a combination of biological and chemical transformations.All of these reactions can be modeled using first-order kinetics; optionalalgorithms can be used for plant uptake of N and immobilization of organic N. Theoptimum first-order kinetic rate parameter is corrected for soil temperatures below35 degrees C by the generalized equation:
KK = K*TH**(TMP-35.0) (3)
where: KK = temperature-corrected first-order reaction rate (/interval) K = optimum first-order reaction rate at 35 degrees C (/interval) TH = temperature correction coefficient for reaction (typically about 1.06) TMP = soil layer temperature (degrees C)
Module Section NITR
107
When temperatures are greater than 35 degrees C, the rate is considered optimum,that is, KK is set equal to K. When the temperature of the soil layer is below 4degrees C or the layer is dry, no biochemical transformations occur.
The corrected reaction rate parameters are determined every biochemical reactioninterval and multiplied by the respective storages as shown in Figure 4.2(1).10-1to obtain the reaction fluxes. Plant uptake can vary monthly and can bedistributed between nitrate and ammonium by the parameters NO3UTF and NH4UTF.These parameters are intended to designate the fraction of plant uptake from eachspecies of N; the sum of NO3UTF and NH4UTF should be 1.0.
The biochemical reaction rate fluxes that are shown in Figure 4.2(1).10-1 arecoupled, that is, added to and subtracted from the storages simultaneously. Thecoupling of the fluxes is efficient in use of computer time, but has a tendency toproduce unrealistic negative storages when large reaction intervals and largereaction rates are used jointly. A method is used which modifies the reactionfluxes so that they do not produce negative storages. A warning message is issuedwhen this modification occurs.
Immobilization of nitrate and ammonia (conversion to particulate labile organic N)can be simulated using either first-order kinetics as described above, or asaturation kinetics (Michaelis-Menten) method. The saturation kinetics option isintended primarily for forests, and is activated when NUPTFG = 2 or -2. Detailsof this option are presented below, under plant uptake optional methods.
Ammonia Volatilization
Ammonia volatilization is included as an optional (AMVOFG = 1) first-order reactionin order to allow large concentrations of ammonia in the soil, resulting fromanimal waste and fertilizer applications, to be attenuated by losses to theatmosphere. The original formulation by Reddy et al., (1979) included adjustmentfor variable soil cation exchange capacity (CEC) and wind speed, and it could be"turned off" after seven days. In HSPF, it is assumed that: (1) the CEC factor canbe incorporated into the first-order rate constant by the user, and (2) the wind(air flow) is always high enough to result in maximum loss; Reddy's original methodreduced the volatilization rate only when wind speed was less than 1.4 km/day.Downward adjustment of the rate, after an initial period of high losses, requiresuse of the Special Actions capability.
The temperature correction for volatilization of ammonia is slightly different thanthe standard method used for the other reactions. The reference temperature isuser-specified, instead of 35 degrees C, since rates in the literature are oftengiven at a temperature of 20 degrees C. Also, instead of attaining a maximum valueat the reference temperature, the volatilization rate is adjusted upwards when thesoil temperature exceeds the reference temperature.
Plant nitrogen
The user can select between two optional scenarios for simulating plant nitrogen.If ALPNFG = 0, plant N is simulated in each of the four standard soil layers (i.e.,surface, upper, lower, and active groundwater). If ALPNFG = 1, plant N is alsosimulated in above-ground and litter compartments, in addition to the standard
Module Section NITR
108
below-ground layers. Plant N simulation involves uptake of ammonium and nitrateby the plant, and "return" of plant N to organic N in the soil. Above-ground plantN returns to the litter compartment, and litter plant N returns to the particulateorganic N compartments in the surface and upper soil layers. These return"reactions" from above-ground plant N to litter and from litter to surface/upperorganic N are simulated using first-order kinetics. No other reactions affectthese nitrogen storages except for plant uptake to the above-ground compartment.
Return of plant N to particulate organic N is divided into labile and refractoryfractions. By using default values of the return parameters, all plant returnbecomes labile organic N.
Optional Methods for Modeling Plant Uptake of Nitrogen
There are three optional methods for simulating plant uptake, including thedefault, first-order method described above. These options are selected using theinput flag NUPTFG.
The second method for simulating plant uptake is to use a yield-based algorithm(NUPTFG = 1). This approach is a modification of the algorithm used in the NitrateLeaching and Economic Analysis Package (NLEAP model) (Shaffer et al, 1991). It isdesigned to be less sensitive to soil nutrient levels and nutrient applicationrates than the first-order rate approach (NUPTFG = 0); thus, it allows crop needsto be satisfied, subject to nutrient and moisture availability, without beingcalculated as a direct function of the soil nutrient level. This approach allowsa better representation of nutrient management practices, since uptake levels willnot change dramatically with changes in application rates.
In this method, a total annual target, NUPTGT, is specified by the user, and isthen divided into monthly targets during the crop growing season; the target isfurther divided into the four soil layers. The monthly target for each soil layeris calculated as:
MONTGT= NUPTGT* NUPTFM(MON)* NUPTM(MON)* CRPFRC(MON,ICROP) (4)
where: MONTGT= monthly plant uptake target for current crop (lb N/ac or kg N/ha) NUPTGT= total annual uptake target (lb N/ac or kg N/ha) NUPTFM= monthly fraction of total annual uptake target (-) NUPTM = soil layer fraction of monthly uptake target (-) CRPFRC= fraction of monthly uptake target for current crop (-)
This is 1.0 unless the month contains parts of two or more crop seasons, in which case the monthly uptake target is divided among the crops according to the number of days of the month belonging to each season. MON = current month ICROP = index for current crop
Planting and harvesting dates can be specified for up to three separate cropsduring the year. Plant uptake is assumed to occur only during a growing season,defined as the time period between planting and harvest. When portions of two
Module Section NITR
109
growing seasons are contained within one month, the total monthly target is dividedbetween the two crops in proportion to the number of days in each season in thatmonth. The daily target is calculated by starting at zero at the beginning of acrop season and using a trapezoidal rule to solve for monthly boundaries; linearinterpolation is used to solve for daily values between the monthly boundaries, andbetween a monthly boundary and a planting or harvest date.
Yield-based plant uptake only occurs when the soil moisture is above the wiltingpoint, which is specified by the user for each soil layer. No temperature rateadjustment is performed, but all uptake is stopped when soil temperature is below4 degrees C. If the uptake target is not met during a given interval, whether fromnutrient, temperature, or moisture stress, then an uptake deficit is accumulated,and applied to the next interval's target. When uptake later becomes possible, theprogram will attempt to make up the deficit by taking up nitrogen at a rate higherthan the normal daily target, up to a user-specified maximum defined as a multipleof the target rate. The deficit is tracked for each soil layer, and is reset tozero at harvest, i.e. it does not carry over from one crop season to the next.
When using the yield-based plant uptake option, it is also possible to representleguminous plants (e.g. soybeans) that will fix nitrogen from the atmosphere. Thealgorithm is designed to allow N fixation only to make up any shortfall in soilnitrogen, i.e., fixation is only allowed if the available soil nitrogen (nitrateand solution ammonium) is insufficient to satisfy the target uptake. The maximumdaily nitrogen fixation rate is subject to the same limits as the uptake underdeficit conditions noted above.
The third option for simulating plant uptake is to use a Michaelis-Menten orsaturation kinetics method. This algorithm is included in HSPF primarily forsimulating forested areas, and whenever it is selected, the same method is alsoused to simulate immobilization of ammonium and nitrate. Saturation kinetics isactivated for both uptake and immobilization by setting NUPTFG to 2 or -2.
The user specifies a maximum rate and a half-saturation constant for each of thefour processes (uptake of nitrate and ammonia, and immobilization of nitrate andammonia). The input maximum rates can vary monthly. The corresponding reactionfluxes are computed using the general equation:
FLUX = KK * CONC / (CS + CONC) (5)
where: FLUX = amount of flux (mg/l/interval) KK = temperature corrected maximum rate (mg/l/interval) CONC = concentration of nitrogen species in soil layer (mg/l) CS = half-saturation constant (mg/l)
The flux is then converted to units of mass per interval.
Regardless of the option used to simulate plant uptake, if the above-ground andlitter compartments are being simulated, then the user can specify the fraction ofuptake from each layer that goes to the above-ground plant N storage. Theremainder is assumed to become plant N within that soil layer.
Module Section PHOS
110
4.2(1).11 Simulate Phosphorous Behavior in Detail (Section PHOS of Module PERLND)
Purpose
Module section PHOS simulates the behavior of phosphorus in a pervious landsegment. This involves modeling the transport, plant uptake, adsorption/desorption, immobilization, and mineralization of the various forms of phosphorus.Because phosphorus is readily tied to soil and sediment, it is usually scarce instreams and lakes. In fact, in many cases it is the limiting nutrient in theeutrophication process. Because of its scarcity, accurate simulation isparticularly important.
Method
The method used to transport and react phosphorus is the same as that used fornitrogen in module section NITR. The subroutines used to transport phosphorus aredescribed in module section PEST. Organic phosphorus and adsorbed phosphate areremoved with sediment by calling subroutine SEDMOV. Phosphate in solution istransported in the moving water using subroutines TOPMOV and SUBMOV. Phosphorusreactions are simulated in the soil by subroutine PHORXN.
Inputs of the two species of phosphorus to the surface and upper soil layers,natural or agricultural, can be simulated using either or both of two methods:1) as changes to storage variables in the SPEC-ACTIONS block, or 2) as atmosphericdeposition.
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forPERLND, and are specified in the EXT SOURCES block of the UCI. The monthly valuesare input in the MONTH-DATA block in the UCI.
If atmospheric deposition data are input to the model, the soil storage is updatedfor each of the two species of phosphorus for both affected soil layers using theformula:
P = P + ADFX + PREC*ADCN (1)
where: P = storage of phosphorus species in the soil layer (mass/area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation depth ADCN = concentration for wet atmospheric deposition (mass/volume)
Module Section PHOS
111
In subroutine PHORXN, phosphate is adsorbed and desorbed by either first-orderkinetics or by the Freundlich method. The mechanics of these methods are describedin module section PEST. As with the simulation of ammonium adsorption/desorption,the frequency of this chemical reaction for phosphate can also be specified.Unlike ammonium, typically phosphate includes a large portion which is not attachedto the soil particle but is combined with cations. This is because phosphate ismuch less soluble with the ions found in soils than ammonium.
Other reactions performed by subroutine PHORXN include mineralization,immobilization, and plant uptake. These are accomplished using temperaturedependent, first-order kinetics; the same method used for the nitrogen reactions.As for nitrogen, a yield-based plant uptake option is available for phosphorus andis activated with PUPTFG = 1. The saturation-kinetics option for uptake andimmobilization, however, is not available for phosphorus. The only otherdifference between nitrogen and phosphorus plant uptake is that only solutionphosphate can be taken up by the plant and no fixation process is modeled. Thegeneral description of these processes is in module section NITR. Figure4.2(1).11-1 shows the parameters and equations used to calculate the reactionfluxes for phosphorus. Reactions are simulated for each of the four soil layersusing separate parameter sets for each layer. As with nitrogen, the biochemicalphosphate reaction fluxes of mineralization, immobilization, and plant uptake canbe determined at an interval less frequent than the basic simulation interval.
Module Section PHOS
112
Figure 4.2(1).11-1 Flow diagram for phosphorus reactions
Module Section TRACER
113
4.2(1).12 Simulate Movement of a Tracer (Section TRACER of Module PERLND)
Purpose
The purpose of this code is to simulate the movement of any nonreactive tracer(conservative) in a pervious land segment. Chloride, bromide, and dyes arecommonly used tracers which can be simulated by section TRACER. Also, totaldissolved salts could possibly be modeled by this section. Typically, this codeis applied to chloride to calibrate solute movement through the soil profile. Thisinvolves adjustment of the percolation retardation factors (see section MSTLAY)until good agreement with observed chloride concentrations has been obtained. Oncethese factors have been calibrated, they are used to simulate the transport ofother solutes, such as nitrate.
Method of Simulating Tracer Transport
Tracer simulation uses the agri-chemical solute transport subroutines TOPMOV andSUBMOV which are described in section PEST. No reactions are modeled.
Inputs of the tracer substance to the surface and upper soil layers can besimulated using either or both of two methods: 1) as changes to storage variablesin the SPEC-ACTIONS block, or 2) as atmospheric deposition.
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forPERLND, and are specified in the EXT SOURCES block of the UCI. The monthly valuesare input in the MONTH-DATA block in the UCI.
If atmospheric deposition data are input to the model, then the soil storage oftracer is updated for both affected soil layers using the formula:
T = T + ADFX + PREC*ADCN (1)
where: T = storage of tracer in the soil layer (mass/area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation depth ADCN = concentration for wet atmospheric deposition (mass/volume)
Module IMPLND
114
4.2(2) Simulate an Impervious Land Segment (Module IMPLND)
In an impervious land segment, little or no infiltration occurs. However, landsurface processes do occur as illustrated in Figure 4.2(2)-1. Snow may accumulateand melt, and water may be stored or may evaporate. Various water qualityconstituents accumulate and are removed. Water, solids, and various pollutantsflow from the segments by moving laterally to a downslope segment or to areach/reservoir.
Module IMPLND simulates these processes. The sections of IMPLND and theirfunctions are given in the structure chart shown in Figure 4.2(2)-2. They areexecuted from left to right. Many of them are similar to the correspondingsections in the PERLND module. In fact, since sections SNOW and ATEMP performfunctions that can be applied to pervious or impervious segments, they are sharedby both modules. IWATER is analogous to PWATER in module PERLND; SOLIDS isanalogous to SEDMNT; IWTGAS is analogous to PWTGAS; and IQUAL is analogous toPQUAL. However, the IMPLND sections are simpler since they contain no infiltrationfunction and consequently no subsurface flows.
4.2(2).3 Simulate the Water Budget for an Impervious Land Segment (Section IWATER of Module IMPLND)
Purpose
Section IWATER simulates the retention, routing, and evaporation of water from animpervious land segment.
Method
Section IWATER is similar to section PWATER of the PERLND module. However, IWATERis simpler because there is no infiltration and consequently no subsurfaceprocesses. IWATER is composed of the parent subroutine plus three subordinatesubroutines: RETN, IROUTE, and EVRETN. RETN is analogous to ICEPT, IROUTE isanalogous to PROUTE, and EVRETN is analogous to EVICEP in module section PWATER.The time series requirements are the same as for section PWATER.
Figure 4.2(2).3-1 schematically represents the fluxes and storages simulated inmodule section IWATER. Moisture (SUPY) is supplied by precipitation, or under snowconditions, it is supplied by the rain not falling on the snowpack plus the wateryielded by the snowpack. This moisture is available for retention; subroutine RETNperforms the retention functions. Lateral surface inflow (SURLI) may also beretained if the user so specifies by setting the flag parameter RTLIFG=1.Otherwise, retention inflow (RETI) equals SUPY. Moisture exceeding the retentioncapacity overflows the storage and is available for runoff.
Module IMPLND
115
Figure 4.2(2)-1 Impervious land segment processes
Module IMPLND
116
Figure 4.2(2)-2 Structure chart for IMPLND Module
Module IMPLND
117
Figure 4.2(2).3-1 Hydrologic Processes
Module Section IWATER
118
The retention capacity, defined by the parameter RETSC, can be used to designateany retention of moisture which does not reach the overland flow plane. RETSC maybe used to represent roof top catchments, asphalt wetting, urban vegetation,improper drainage, or any other containment of water that will never flow from theland segment. The user may supply the retention capacity on a monthly basis toaccount for seasonal variations, or may supply one value designating a fixedcapacity.
Water held in retention storage is removed by evaporation (IMPEV). The amountevaporated is determined in subroutine EVRETN. Potential evaporation is an inputtime series.
Retention outflow (RETO) is combined with any lateral inflow when RTLIFG=0producing the total inflow to the detention storage (SURI). Water remaining in thedetention storage plus any inflow is considered the moisture supply. The moisturesupply is routed from the land surface in subroutine IROUTE.
4.2(2).3.2 Determine How Much of the Moisture Supply Runs Off (subroutine IROUTE)
Purpose
The purpose of subroutine IROUTE is to determine how much of the moisture supplyruns off the impervious surface in one simulation interval.
Method of Routing
A method similar to that used in module PERLND (Section 4.2(1).3.2.1.3) is employedto route overland flow.
Module Section SOLIDS
119
4.2(2).4 Simulate Accumulation and Removal of Solids (Section SOLIDS of Module IMPLND)
Purpose
Module section SOLIDS simulates the accumulation and removal of solids by runoffand other means from the impervious land segment. The solids outflow may be usedin section IQUAL to simulate quality constituents associated with particulates.
Method
The equations used in this section are based on those in the NPS Model (Donigianand Crawford, 1976b). Figure 4.2(2).4-1 schematically represents the fluxes andstorages simulated by section SOLIDS. Lateral input of solids by water flow is auser designated option. Washoff of solids may be simulated by one of two ways.One subroutine is similar to the method used in the NPS Model. However, thismethod is dimensionally nonhomogeneous. That is, a flux and a storage are addedmaking the answer more interval dependent. This technique was designed for 15-minintervals. The other subroutine is dimensionally homogenous, since only a fluxterm is used in the solution.
The accumulation and removal of solids which occurs independently of runoff (e.g.,by atmospheric fallout, street cleaning) is handled in subroutine ACCUM.
4.2(2).4.1 Washoff Solids Using Method 1 (subroutine SOSLD1)
Purpose
Subroutines SOSLD1 and SOSLD2 perform the same task, but by different methods.They simulate the washoff of solids from an impervious land segment.
Method
When simulating the washoff of solids, the transport capacity of the overland flowis estimated and compared to the amount of solids available. The transportcapacity is calculated by the equation:
STCAP = DELT60*KEIM*((SURS + SURO)/DELT60)**JEIM (1)
where: STCAP = capacity for removing solids (tons/ac per interval) DELT60 = hours per interval KEIM = coefficient for transport of solids SURS = surface water storage (inches) SURO = surface outflow of water (in/interval) JEIM = exponent for transport of solids
Module Section SOLIDS
120
Figure 4.2(2).4-1 Flow diagram of the SOLIDS section of the IMPLND Application Module
Module Section SOLIDS
121
When STCAP is greater than the amount of solids in storage, washoff is calculatedby:
SOSLD = SLDS*SURO/(SURS + SURO) (2) If the storage is sufficient to fulfill the transport capacity, then the followingrelationship is used:
SOSLD = STCAP*SURO/(SURS + SURO) (3)
where: SOSLD = washoff of solids (tons/ac per interval) SLDS = solids storage (tons/ac)
SOSLD is then subtracted from SLDS.
Subroutine SOSLD1 differs from SOSLD2 in that it uses the dimensionallynonhomogeneous term (SURS + SURO)/DELT60 in the above equations, while SOSLD2 usesthe homogeneous term SURO/DELT60.
4.2(2).4.2 Washoff Solids Using Method 2 (subroutine SOSLD2)
Purpose
The purpose of this subroutine is the same as SOSLD1. It only differs in method.
Method of Determining Removal
Unlike subroutine SOSLD1, this method of determining sediment removal makes use ofthe dimensionally homogeneous term SURO/DELT60 instead of (SURO+SURS)/DELT60 in thefollowing equation.
STCAP = DELT60*KEIM*(SURO/DELT60)**JEIM (4)
When STCAP is more than the amount of solids in storage, the flow washes off allof the solids storage (SLDS). However, when STCAP is less than the amount ofsolids in storage, the situation is transport limiting, so SOSLD is equal to STCAP.
Module Section SOLIDS
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4.2(2).4.3 Accumulate and Remove Solids Independently of Runoff (subroutine ACCUM)
Purpose
Subroutine ACCUM simulates the accumulation and removal of solids independently ofrunoff; for example, atmospheric fallout and street cleaning.
Method
The storage is updated once a day, on those days when precipitation did not occurduring the previous day, using the equation:
SLDS = ACCSDP + SLDSS*(1.0 - REMSDP) (5)
where: ACCSDP = accumulation rate of the solids storage (tons/ac per day) SLDS = solids in storage at end of day (tons/ac) SLDSS = solids in storage at start of day (tons/ac) REMSDP = unit removal rate of solids in storage (i.e., fraction removed per day)
ACCSDP and REMSDP may be input on a monthly basis to account for seasonalvariations.
Note: if no runoff occurs, Equation 5 will cause the solids storage toasymptotically approach a limiting value. The limit, found by setting SLDS andSLDSS to the same value (SLDSL), is:
SLDSL = ACCSDP/REMSDP (6)
Module Section IWTGAS
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4.2(2).5 Estimate Water Temperature and Dissolved Gas Concentrations (Section IWTGAS of Module IMPLND)
Purpose
IWTGAS estimates the water temperature and concentrations of dissolved oxygen andcarbon dioxide in the outflow from the impervious land segment.
Method
Outflow temperature is estimated by the following regression equation:
SOTMP = AWTF + BWTF*AIRTC (1)
where: SOTMP = impervious surface runoff temperature (degrees C) AWTF = Y-intercept BWTF = slope AIRTC = air temperature (degrees C)
The parameters AWTF and BWTF may be input on a monthly basis. When snowmeltcontributes to the outflow, SOTMP is set equal to 0.5.
The dissolved oxygen and carbon dioxide concentrations of the overland flow areassumed to be at saturation and are calculated as direct functions of watertemperature. IWTGAS uses the following empirical nonlinear equation to relatedissolved oxygen at saturation to water temperature (Committee on SanitaryEngineering Research, 1960):
SODOX = (14.652 + SOTMP*(-0.41022 + SOTMP*(0.007991 - 0.000077774*SOTMP)))*ELEVGC (2)
where: SODOX = concentration of dissolved oxygen in surface outflow (mg/l) SOTMP = surface outflow temperature (degrees C) ELEVGC = correction factor for elevation above sea level (ELEVGC is calculated by the Run Interpreter dependent upon mean elevation of the segment)
The empirical equation for dissolved carbon dioxide concentration of the overlandflow (Harnard and Davis, 1943) is:
SOCO2 = (10**(2385.73/ABSTMP - 14.0184 + 0.0152642*ABSTMP)) (3) *0.000316*ELEVGC*12000.0
where: SOCO2 = concentration of dissolved carbon dioxide in surface outflow (mg C/l) ABSTMP = absolute temperature of surface outflow (degrees K)
Module Section IQUAL
124
4.2(2).6 Simulate Washoff of Quality Constituents Using Simple Relationships with Solids and Water Yield (Section IQUAL of Module IMPLND)
Purpose
The IQUAL module section simulates water quality constituents or pollutants in theoutflows from an impervious land segment using simple relationships with wateryield and/or solids. Any constituent can be simulated by this module section. Theuser supplies the name, units and parameter values appropriate to each of theconstituents that are needed in the simulation. Note that more detailed methodsof simulating solids, heat, dissolved oxygen, and dissolved carbon dioxide removalfrom the impervious land segment are available in other sections of IMPLND.
Approach
The basic algorithms used to simulate quality constituents are a synthesis of thoseused in the NPS Model (Donigian and Crawford, 1976b) and HSP QUALITY (Hydrocomp,1977). However, some options and combinations are unique to HSPF.
Figure 4.2(2).6-1 shows schematically the fluxes and storages represented in modulesection IQUAL. A quality constituent may be simulated by two methods. Oneapproach is to simulate the constituent by association with solids removal. Theother approach is to simulate it by using atmospheric deposition and/or basicaccumulation and depletion rates together with depletion by washoff; that is,constituent outflow from the surface is a function of the water flow and theconstituent in storage. A combination of the two methods may be used in which theindividual fluxes are added to obtain the total surface outflow. These approacheswill be discussed further in the descriptions of the corresponding subroutines.
IQUAL allows the user to simulate up to 10 quality constituents at a time. If aconstituent is considered to be associated with solids, it is called a QUALSD. Thecorresponding term for constituents associated directly with overland flow isQUALOF. Each of the 10 constituents may be defined as either a QUALSD or a QUALOFor both. Note that only a QUALOF may receive atmospheric deposition, since it isthe only type to maintain a storage. However, no more than seven of any one ofthe constituent types (QUALSD or QUALOF) may be simulated in one operation. Theprogram uses a set of flag pointers to keep track of these associations. Forexample, QSDFP(3)=0 means that the third constituent is not associated with solids,whereas QSDFP(6)=4 means that the sixth constituent is the fourth solids associatedconstituent (QUALSD). Similar flag pointer arrays are used to indicate whether ornot a quality constituent is a QUALOF.
4.2(2).6.1 Remove by Association with Solids (subroutine WASHSD)
Purpose
WASHSD simulates the removal of a quality constituent from the impervious landsurface by association with the solids removal determined in section SOLIDS.
Module Section IQUAL
125
Figure 4.2(2).6-1 Flow diagram for IQUAL section of IMPLND Application Module
Module Section IQUAL
126
Method
This approach assumes that the particular quality constituent removed from the landsurface is in proportion to the solids removal. The relation is specified byuser-input "potency factors." Potency factors indicate the constituent strengthrelative to the solids removal from the surface. For each quality constituentassociated with solids, the user supplies separate potency factors. The user isalso able to supply monthly potency factors for constituents that vary somewhatconsistently throughout the year.
Removal of the solids associated constituent by solids washoff is simulated by:
SOQS = SOSLD*POTFW (1)
where: SOQS = flux of constituent associated with solids washoff (quantity/ac per interval) SOSLD = washoff of detached solids (tons/ac per interval) POTFW = washoff potency factor (quantity/ton)
The unit "quantity" refers to mass units (pounds or tons in the English system) orsome other quantity, such as number of organisms for coliforms. The user specifiesthe units of "quantity."
4.2(2).6.2 Accumulate and Remove by a Constant Unit Rate and by Overland Flow (subroutine WASHOF)
Purpose
WASHOF simulates the accumulation of a quality constituent on the impervious landsurface and its removal by a constant unit rate and by overland flow.
Method
This subroutine differs from subroutine WASHSD in that the storage of the qualityconstituent is simulated. The stored constituent can be accumulated and removedby processes which are independent of storm events, such as cleaning, decay, andwind deposition, and it is washed off by overland flow. The accumulation andremoval rates can have monthly values to account for seasonal fluctuations. Theconstituent may, alternatively or additionally, receive atmospheric deposition.
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If it is in the formof a concentration in rainfall, then it is considered "wet deposition", and HSPFautomatically combines it with the input rainfall time series to compute theresulting flux. Either type of deposition data can be input as a time series,which covers the entire simulation period, or as a set of monthly values that isused for each year of the simulation. The atmospheric deposition time series aredocumented in the EXTNL table of the Time Series Catalog for IMPLND, and are inputin the EXT SOURCES block. The monthly values are input in the MONTH-DATA block.
Module Section IQUAL
127
If atmospheric deposition data are input, the surface storage is updated:
SQO = SQO + ADFX + PREC*ADCN (1)
where: SQO = storage of available quality constituent on the surface (mass/area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation depth ADCN = concentration for wet atmospheric deposition (mass/volume)
When there is surface outflow and some quality constituent is in storage, thenwashoff is simulated using the commonly used relationship:
SOQO = SQO*(1.0 - EXP(-SURO*WSFAC)) (2)
where: SOQO = washoff of the quality constituent from the land surface (quantity/ac per interval) SQO = storage of the quality constituent on the surface (quantity/ac) SURO = surface outflow of water (in/interval) WSFAC = susceptibility of the quality constituent to washoff (/inch) EXP = exponential function
The storage is updated once a day to account for accumulation and removal whichoccurs independent of runoff by the equation:
SQO = ACQOP + SQOS*(1.0 - REMQOP) (3)
where: ACQOP = accumulation rate of the constituent (quantity/ac per day) SQOS = SQO at the start of the interval REMQOP = unit removal rate of the stored constituent (per day)
The Run Interpreter computes REMQOP and WSFAC for this subroutine according to:
REMQOP = ACQOP/SQOLIM (4)
where: SQOLIM = asymptotic limit for SQO as time approaches infinity, if no washoff occurs (quantity/ac), and
WSFAC = 2.30/WSQOP (5)
where: WSQOP = rate of surface runoff that results in 90% washoff in 1 hour (in/hr)
Since the unit removal rate (REMQOP) is computed from two other parameters, it isnot supplied directly by the user.
Module RCHRES
128
4.2(3) Simulate a Free-flowing Reach or Mixed Reservoir (Module RCHRES)
This module simulates the processes which occur in a single reach of open or closedchannel or a completely mixed lake. For convenience, such a processing unit isreferred to as a RCHRES throughout this documentation. In keeping with theassumption of complete mixing, the RCHRES consists of a single zone situatedbetween two nodes, which are the extremities of the RCHRES.
Flow through a RCHRES is assumed to be unidirectional. The inflow and outflow ofmaterials through a RCHRES are illustrated in Figure 4.2(3)-1. Water and otherconstituents which arrive from other RCHRES's and local sources enter the RCHRESthrough a single gate (INFLO). Outflows may leave the RCHRES through one ofseveral gates or exits (OFLO). A RCHRES can have up to five OFLO exits.Precipitation, evaporation, and other fluxes also influence the processes whichoccur in the RCHRES, but do not pass through the exits.
The ten major subdivisions of the RCHRES module and their functions are shown inFigure 4.2(3)-2. RPTOT, RBAROT, and RPRINT perform the storage and printout ofresults from the other module sections of RCHRES (HYDR through RQUAL). Within amodule section, simulation of physical processes (longitudinal advection, sinking,benthal release) is always performed before simulation of biochemical processes.
The user specifies which module sections are active. If any "quality" sections(CONS through RQUAL) are active, section ADCALC must also be active; it computescertain quantities needed to simulate advection of the quality constituents.Besides fulfilling this requirement, the user must ensure that all the time seriesrequired by the active sections are available, either as supplied input time seriesor as data computed by another module section. For example, if RQUAL is active,the water temperature must be supplied, either as an input time series or byactivating section HTRCH which will compute it.
Water Rights Categories
The HYDR section of RCHRES allows the optional simulation of water "categories",which are used to facilitate the modeling of water rights or ownership. If thisoption is turned on (by including the CATEGORY block in the UCI), each RCHRES inthe run keeps track of the user-defined categories of all inflows, storages, andoutflows, as well as precipitation and evaporation fluxes. Up to 100 categoriescan be specified in the CATEGORY block. Details of this option are provided in thediscussion of HYDR below (Section 4.2(3).1), and the CATEGORY block documentation(Section 4.12 of Part F). Also, refer to the description of HYDR inputs in Section4.4(3).2 of Part F.
Module RCHRES
129
Figure 4.2(3)-1 Flow of materials through a RCHRES
Module RCHRES
130
Figure 4.2(3)-2 Structure chart for RCHRES Module
Module RCHRES
131
4.2(3).01 Simulate Sinking of Suspended Material (subroutine SINK)
Purpose SINK calculates the quantity of material settling out of a RCHRES and determinesthe resultant change in concentration of the material within the RCHRES.
Method
The portion of material settling out of a RCHRES during an interval is calculatedby the equation: SNKOUT = CONC*(KSET/AVDEPE) (1) where: SNKOUT = fraction of material which settles out (reduction of concentration/interval) CONC = concentration of material before deposition KSET = sinking rate (ft/interval) (dependent upon RCHRES characteristics and type of material) AVDEPE = average depth of water (feet) In any interval in which KSET is greater than AVDEPE, all the material in theRCHRES sinks out of the water.
The mass of material sinking out of the RCHRES is calculated as: SNKMAT = SNKOUT*VOL (2) where: SNKMAT = mass of material that settles out during the interval (mass.ft3/l/interval or mass.m3/l/interval) VOL = volume of water in RCHRES (ft3 or m3)
Module Section HYDR
132
4.2(3).1 Simulate Hydraulic Behavior (Section HYDR of Module RCHRES)
Purpose
The purpose of this code is to simulate the hydraulic processes occurring in areach or a mixed reservoir (RCHRES). The final goal of the process may be to routefloods, study reservoir behavior, or analyze constituents dissolved in the water.
Schematic View of Fluxes and Storage
Figure 4.2(3).1-1 shows the principal state variable (stored volume) and fluxeswith which this part of HSPF deals. All water entering the RCHRES from surface and subsurface sources arrives through"gate" INFLO; this quantity is called IVOL. The user indicates the time serieswhich enter this gate in the EXT SOURCES or NETWORK Block in the User's ControlInput (UCI). If no time series are specified, the system assumes the RCHRES haszero inflow.
The volume of water which leaves the RCHRES during a simulation time interval,through gate OFLO(N), is called OVOL(N). The total outflow is ROVOL.
The input of water from precipitation falling directly on the water surface and theloss of water by evaporation from the surface can also be considered. The useractivates these options by supplying the time series PREC and/or POTEV in theUser's Control Input (External Sources block). These time series are in units ofdepth/interval. The code multiplies these quantities by the current surface areaof the RCHRES to obtain volumes of input or output. If either time series isabsent from the UCI it is assumed that the option is inactive and the correspondingflux is zero.
The basic equation is that of continuity: VOL - VOLS = IVOL + PRSUPY - VOLEV - ROVOL (1) where: VOL = volume at the end of the interval VOLS = volume at the start of the interval
This can be written as:
VOL = VOLT - ROVOL (2) where: VOLT = IVOL + PRSUPY - VOLEV + VOLS The principal task of this subroutine is to estimate ROVOL and, hence, the volumeat the end of the interval (VOL).
Module Section HYDR
133
Figure 4.2(3).1-1 Flow diagram for the HYDR Section of the RCHRES Application Module
Module Section HYDR
134
Calculation of Outflows and VOL
If water is available, it is assumed that the total volume of water leaving aRCHRES in an interval is:
ROVOL = (KS*ROS + COKS*ROD)*DELTS (3) where: KS = weighting factor (0 <= KS <= 0.99) COKS = 1.0 - KS (complement of KS) ROS = total rate of outflow from the RCHRES at the start of the interval ROD = total rate of demanded outflow for the end of the interval DELTS = simulation interval in seconds
That is, the mean rate of outflow is assumed to be a weighted mean of the rates atthe start and end of the interval. The weighting factor KS is supplied either bythe user or by default. Care should be exercised in selecting a value because, asKS increases from 0.0 to 1.0, there is an increasing risk that the computation ofoutflow rates will become unstable. Theoretically, a value of 0.5 gives the mostaccurate results, provided oscillations do not occur. The default value of 0.0 haszero risk, but gives less accurate results. Users are advised to be very carefulif a nonzero value is used; users should not select a value greater than 0.5. Combination of Equations 2 and 3 yields:
VOL = VOLT - (KS*ROS + COKS*ROD)*DELTS (4)
There are two unknown values in this equation: VOL and ROD. Thus, a secondrelation is required to solve the problem. To provide this function, it is assumedthat outflow demands for the individual exits are of the form:
OD(1) = f1(VOL,t) OD(2) = f2(VOL,t) . . OD(NEXITS) = fNEXITS(VOL,t) (5)
That is, the outflow demand for each exit is a function of volume or time or acombination. This topic is discussed in greater detail in Section 4.2(3).1.1.2.It follows that the total outflow demand is of similar form:
ROD = funct(VOL,t) (6)
At a given time in the simulation, t is known and the above functions reduce to: OD(N) = fN(VOL) (7) ROD = funct(VOL) (8)
Equation 8 provides the second relation required to solve the problem.
Module Section HYDR
135
Equations 4, 7, and 8 are shown in Figure 4.2(3).1-2. The point of intersectionof Equations 4 and 8 gives the values RO, VOL, and hence O(1), O(2), etc.
where: RO = total rate of outflow from the RCHRES at the end of the interval O(N) = rate of outflow through exit N at the end of the interval
In HSPF, it is assumed that each outflow demand can be represented by one or bothof the following types of components:
Component = function(VOL). This is most useful in simulating RCHRES's wherethere is no control over the flow or where gate settings are only a functionof water level. This component is supplied to the program in the form of anFTABLE.
Component = function(time). This is most useful for handling demands formunicipal, industrial, or agricultural use. The function may be cyclic (forexample, annual cycle) or general (for example, annual cycle superimposed onan increasing trend). The user must supply this component in the form of aninput time series.
If a user indicates that both types of component are present in an outflow demand,then the user must also specify how they are to be combined to get the demand.HSPF allows the following options:
1. OD(N) = Min [fN(VOL),gN(t)]. This is useful in cases such as the following:
Suppose a water user has an optimum demand which may be expressed as afunction of time (g(t)); however, his pump has a limited capacity to deliverwater. This capacity is a function of the water level in the RCHRES fromwhich the pump is drawing the water. Thus, it can be expressed as a functionof the volume in the RCHRES (f(VOL)). Then, his actual demand for water willbe the minimum of the two functions. Note that g(t) is an input time series(OUTDGT). See the Time Series Catalog (Section 4.7).
2. OD(N) = Max [fN(VOL),gN(t)]
3. OD(N) = fN(VOL) + gN(t)
If one or more outflow demands have an f(VOL) component (Fig.4.2(3).1-2a),subroutine ROUTE is called to solve the routing equations. In this case, theevaluation of the outflow demands and the solution of the equations can be quitecomplicated.
If there is no f(VOL) component in any demand, the process is much simpler (Figure4.2(3).1-2b). Subroutine NOROUT is called in this case.
Module Section HYDR
136
Figure 4.2(3).1-2 Graphical representation of the equations used to compute outflow rates and volume
Module Section HYDR
137
Figure 4.2(3).1-3 Typical RCHRES configurations and the method used to represent geometric and hydraulic properties
Module Section HYDR
138
Representing the Geometry and Hydraulic Properties of a RCHRES
HSPF makes no assumptions regarding the shape of a RCHRES. It does not requirethat the cross-section be trapezoidal or even that the shape be prismoidal. Thisis one reason why both free flowing reaches and reservoirs can be handled by thesame application module. Both of the shapes shown in Figure 4.2(3).1-3a areacceptable. However, HSPF does assume that:
1. There is a fixed relation between depth (at the deepest point in the RCHRES),surface area, and volume.
2. For any outflow demand with an f(VOL) component, the functional relation isconstant in time (with the exception discussed in Section 4.2(3).2.1.1).
These assumptions rule out cases where the flow reverses direction or where oneRCHRES influences another upstream of it in a time-dependent way. No account istaken of momentum. The routing technique falls in the class known as "storagerouting" or "kinematic wave" methods.
The user specifies the properties of a RCHRES in a table called RCHTAB (Figure4.2(3).1-3b). It has columns for the depth, surface area, volume, and volumedependent functions (fN(VOL)). Each row contains values appropriate to a specifiedwater surface elevation. The system obtains intermediate values by interpolation.Thus, the number of rows in RCHTAB depends on the size of the cross section and thedesired resolution. The table is either included in the User's Control Input (inthe function tables (FTABLES) block) or it may be stored in a Watershed DataManagement (WDM) file. A subsidiary, stand-alone program can be used to generatethis table for RCHRES's with simple properties (for prismoidal channels withuniform flow, use Manning's equation).
Auxiliary Variables Besides calculating outflow rates and the volume in a RCHRES, HSPF can compute thevalues of some auxiliary state variables:
1. If AUX1FG=1, DEP, STAGE, SAREA, AVDEP, TWID, and HRAD are computed, where: DEPis the depth at the deepest point; STAGE is the water stage at a relatedpoint; SAREA is the surface area of water in the RCHRES; AVDEP is the averagedepth (volume/surface area); TWID is the top width (surface area/length); HRADis the hydraulic radius.
2. If AUX2FG=1, AVSECT and AVVEL are computed, where: AVSECT is the average crosssection (volume/length); AVVEL is the average velocity (discharge/AVSECT).
3. If AUX3FG=1, USTAR and TAU are computed, where: USTAR is the bed shearvelocity; TAU is the bed shear stress.
Note that these are point-valued time series; that is, they apply at the boundaries(start or end) of simulation time intervals.
The user specifies whether AUX1FG, AUX2FG, and AUX3FG are ON or OFF. If certain
Module Section HYDR
139
constituents are being simulated, one or more of these flags might be required tobe ON. For example, simulation of oxygen (group OXRX) requires that both AUX1FGand AUX2FG be ON. AUX3FG must be ON if sediment is simulated (group SEDTRN).
4.2(3).1.1 Calculate Outflows Using Hydraulic Routing (subroutine ROUTE)
Purpose
ROUTE computes the rates and volumes of outflow from a RCHRES and the new volumein cases where at least one outflow demand has an f(VOL) component.
Method
The problem is to solve simultaneously Equations 4 and 8. The cases which ariseare shown graphically in Figure 4.2(3).1-4. Equations 7 and 8 are represented bya series of straight line segments. The breakpoints in the lines correspond to arow of entries in RCHTAB (the FTABLE). A segment of Equation 8 can be representedby the equation:
(VOL - V1)/(ROD - ROD1) = (V2 - V1)/(ROD2 - ROD1) (9)
where V1,V2 are volumes specified in adjacent rows of RCHTAB, for the lower andupper extremities of the straight-line segment, respectively. ROD1 and ROD2 arethe corresponding total outflow demands.
The first step is to find the intercept of Equation 4 on the volume axis:
VOLINT = VOLT - KS*ROS*DELTS (10)
If VOLINT is less than zero, the equations cannot be solved (case 3). Equation 4will give a negative value for VOL, even if ROD is zero. Physically, this meansthat we started the interval with too little water to satisfy the projected outflowdemand, even if the outflow rate at the end of the interval is zero. Accordingly,the code does the following:
VOL = 0.0 RO = 0.0 O(*) = 0.0 ROVOL = VOLT
If VOLINT is greater than or equal to zero, the outflow rate at the end of theinterval will be nonzero (case 1 or 2). To determine the case:
1. The intercept of Equation 4 on the Volume axis is found:
OINT = VOLINT/(DELTS*COKS) (11)
2. The maximum outflow demand for which the volume is still zero (RODZ) is found.
Module Section HYDR
140
Figure 4.2(3).1-4 Graphical representation of the work performed by subroutine ROUTE
Module Section HYDR
141
If OINT is greater than RODZ, Equations 4 and 8 can be solved (case 1). Thesolution involves searching for the segment of Equation 8 which contains the pointof intersection of the graphs, and finding the coordinates of the point (RO,VOL).This is done by subroutine SOLVE.
If OINT is less than or equal to RODZ, Equations 4 and 8 cannot be solved (case 2).Physically this means that the RCHRES will instantaneously go dry at the end of theinterval with total outflow rate at that time equal to OINT. Accordingly, the codeassigns a zero value to the RCHRES volume, and the total outflow is equal to theintercept of Equation 4 on the volume axis in Figure 4.2(3).1-4. As many of theindividual demands (O(*)) as possible are satisfied in full by the available water.The remaining water is used to partially satisfy the demand of next highestpriority, and any others are not satisfied at all.
4.2(3).1.1.2 Find the Outflow Demands which Correspond to a Specified Row in RCHTAB (subroutine DEMAND)
Purpose
DEMAND finds the individual and total outflow demands which apply at the end of thepresent interval for a specified level (row) in RCHTAB.
General Method
The approach is to determine the outflow demand for each active exit and accumulatethem to find the total demand.
Evaluating the Demand for Exit N
The outflow demand for an individual exit consists of one or both of twocomponents. Their presence or absence is indicated by two flags:
Component Flag fN(VOL) ODFVFG(N) gN(t) ODGTFG(N)
Finding the fN(VOL) Component
If ODFVFG(N) is zero, there is no fN(VOL) component.
If ODFVFG(N) is greater than zero, there is a fN(VOL) component. The value of theflag is the column number in RCHTAB containing the value to be used to find thecomponent:
Module Section HYDR
142
col = ODFVFG(N) ODFV = fN(VOL) = (column value)*CONVF (12)
where CONVF is a conversion factor which can vary throughout the year. It issupplied by the user in the RCHRES Block of the User's Control Input. It can beused to incorporate effects into the simulation of, for example, seasonal variationin channel roughness.
If ODFVFG(N) is less than zero, there is an fN(VOL) component, but the function fNis time varying. In this case the determination of the component is less direct.The absolute value of ODFVFG(N), say I, gives the element number of a vectorCOLIND() which contains a user-supplied time series. The values in this timeseries indicate which pair of columns in RCHTAB are used to interpolate fN(VOL).For example, if COLIND(I) = 4.6 for a given time step, then the value isinterpolated between those in columns 4 and 5:
ODFV = fN(VOL) = [0.6*(column5 value) + 0.4*(column4 value)]*CONVF (13)
If the user has selected this option, the time series COLIND(I) must be suppliedin the EXT SOURCES Block of the UCI.
This method of outflow demand specification is useful where a set of rule curves(f(VOL)) are specified for releases from a reservoir, and it is necessary to movefrom one curve to another (gradually or suddenly) as time progresses in thesimulation.
Finding the gN(t) Component
If ODGTFG(N) is zero, there is no gN(t) component. If ODGTFG(N) is greater thanzero, there is a gN(t) component. The value of this flag is the element number ofvector OUTDGT() which contains the required time series:
FG2 = ODGTFG(N) ODGT = gN(t) = OUTDGT(FG2) (14)
Combining the fN(VOL) and gN(t) Components
If an outflow demand has both of the components described above, the system expectsthe user to indicate which of the following options to use in combining them:
1. OD(N) = Min [fN(VOL),gN(t)] 2. OD(N) = Max [fN(VOL),gN(t)] 3. OD(N) = fN(VOL) + gN(t) (15)
Module Section HYDR
143
4.2(3).1.1.3 Solve Routing Equations used in Case 1. (subroutine SOLVE)
Purpose
SOLVE finds the point where Equations 4 and 8 intersect (case 1 in Figure4.2(3).1-4).
General Approach
The general idea is to select a segment of Equation 8, and determine the point ofintersection with Equation 4. If this point lies outside the selected segment, thecode will select the adjacent segment (in the direction in which the point ofintersection lies) and repeat the process. This continues until the point lieswithin the segment under consideration. To minimize searching, the segment inwhich the point of intersection was last located is used to start the process.
Solving the Simultaneous Linear Equations
Equations 4 and 9 can be written as:
A1*VOL+ B1*ROD = C1 (16) A2*VOL+ B2*ROD = C2 (17)
These equations can be solved by evaluating the determinants: * A1 B1 * * C1 B1 * * A1 C1 * DET = * * DETV = * * DETO = * * (18) * A2 B2 * * C2 B2 * * A2 C2 * In the code of this subroutine:
FACTA1 = A1 = 1.0/(COKS*DELTS) (19) FACTA2 = A2 = ROD1 - ROD2 (20) FACTB1 = B1 = 1.0 (21) FACTB2 = B2 = V2 - V1 (22) FACTC1 = C1 = OINT (23) FACTC2 = C2 = (V2*ROD1) - (V1*ROD2) (24)
By substituting Equations 19 through 24 in Equation 18, the determinants areevaluated as:
DET = FACTA1*FACTB2 - FACTA2 (25) DETV = OINT*FACTB2 - FACTC2 (26) DETO = FACTA1*FACTC2 - FACTA2*OINT (27)
The coordinates of the point of intersection are: VOL = DETV/DET (28) RO = DETO/DET (29)
Module Section HYDR
144
4.2(3).1.2 Calculate Outflows Without Using Hydraulic Routing (subroutine NOROUT)
Purpose
NOROUT is used to compute the rates and volumes of outflow from a RCHRES and thenew volume in cases where no outflow demand has an f(VOL) component; that is, whereall outflow demands are functions of time only.
Method
Equations 4 and 8 are illustrated for this situation in Figure 4.2(3).1-5. Thesolution procedure is similar to that used in subroutine ROUTE, except that:because no outflow demands depend on volume, no table look-up and interpolation isrequired to evaluate them, and the simultaneous solution of Equations 4 and 8 iseasier.
The intercept of Equation 4 on the volume axis is found, as before, using Equation10. If VOLINT is less than 0.0, there is no solution (case 3). The code takessimilar action to that taken by subroutine ROUTE for this case.
If VOLINT is greater than or equal to 0.0, the solution is either case 1 or case2, as before. In either case, the first step is to evaluate the outflow demands:
FG = ODGTFG(N) OD(N) = OUTDGT(FG) (30) ROD = OD(1) + ... OD(NEXITS) (31)
The intercept of Equation 4 on the volume axis (OINT) is found using Equation 11.If OINT is greater than ROD, Equations 4 and 8 can be solved (case 1):
RO = ROD O(*) = OD(*) (32)
And from Equations 4 and 10,
VOL = VOLINT - COKS*RO*DELTS (33)
If OINT is less than or equal to ROD, Equations 4 and 8 cannot be solved (case 2).The physical meaning and the action taken by the code are identical to thatdescribed for subroutine ROUTE.
Module Section HYDR
145
Figure 4.2(3).1-5 Graphical representation of the work performed by subroutine NOROUT
Module Section HYDR
146
4.2(3).1.3 Compute Values of Auxiliary State Variables (subroutine AUXIL)
Purpose
AUXIL is used to compute the depth, stage, surface area, average depth, top width,and hydraulic radius corresponding to a given volume of water in a RCHRES.
Method of Computing Depth
The basic problem is to interpolate a depth value between those given for discretevalues of volume in RCHTAB. This raises the question of how the interpolationshould be performed; for example, linear or quadratic. Whatever method is used,it should be consistent with the fact that volume is the integral of surface areawith respect to depth.
Most RCHRES's are long and relatively narrow (Figure 4.2(3).1-6). To performinterpolation, it is assumed that surface area varies linearly with depth betweenneighboring levels (rows) in RCHTAB:
SAREA = SA1 + (SA2 - SA1)*RDEP (34)
where SAREA is the surface area at depth DEP; SA1, SA2 are the tabulated values ofsurface area immediately above and below SAREA; RDEP is the relative depth(DEP-DEP1)/(DEP2-DEP1); DEP1, DEP2 are the tabulated values of depth immediatelyabove and below DEP.
By integrating the above equation with respect to depth and equating the result tovolume:
(A*RDEP**2) + (B*RDEP) + C = 0.0 (35)
where: A = SA2 - SA1 B = 2.0*SA1 C = -(VOL - VOL1)/(VOL2 - VOL1)*(B + A)
Equation 35 provides a means of interpolating depth, given volume. There is aquadratic relation between RDEP and VOL. The equation can be solved for RDEPanalytically, but, in HSPF, Newton's method of successive approximations is usedbecause it is generally faster in execution:
1. Calculation starts with an estimate of RDEP: RDEP1 = 0.5 2. The function FRDEP = (A*RDEP1**2) + (B*RDEP1) + C is evaluated 3. The derivative DFRDEP = 2.0*A*RDEP1 + B is evaluated 4. A new value RDEP2 = RDEP1 - FRDEP/DFRDEP is calculated 5. Steps 2-4 are repeated with RDEP1 = RDEP2 until the change in RDEP is small
Module Section HYDR
147
Figure 4.2(3).1-6 Illustration of quantities involved in calculation of depth
Module Section HYDR
148
The depth is found using:
DEP = DEP1 + RDEP2*(DEP2 - DEP1) (36)
Computation of Other State Variables
STAGE is the name for any quantity which differs from DEP by a constant:
STAGE = DEP + STCOR (37)
where: STCOR = the difference, supplied by the user
Surface area is computed using a formula based on Equation 34:
SAREA = SA1 + A*RDEP2 (38)
Average depth is computed as:
AVDEP = VOL/SAREA (39)
The mean top width is found using:
TWID = SAREA/LEN (40)
where: LEN = length of the RCHRES, supplied by the user
The hydraulic radius is calculated as a function of average water depth (AVDEP) andmean top width (TWID):
HRAD = (AVDEP*TWID)/(2.*AVDEP + TWID) (41)
Module Section HYDR
149
4.2(3).1.4 Calculate Bed Shear Stress and Shear Velocity (subroutine SHEAR)
Purpose
SHEAR is used to compute the bed shear velocity and shear stress, based on the meanparticle size of bed sediment and the hydraulic properties of the RCHRES (i.e.,average water depth, average velocity, hydraulic radius, and slope).
The method of calculating shear velocity and shear stress depends on whether theRCHRES is a lake or a river. If the RCHRES is a lake (LKFG=1), shear velocity iscomputed using Equation 8.49 from "Hydraulics of Sediment Transport", by W. H.Graf:
USTAR = AVVEL/(l7.66 + (ALOG10 (AVDEP/(96.5*DB50)))*2.3/AKAPPA) (42)
where: USTAR = shear velocity (ft/s or m/s) AVVEL = average flow velocity (ft/s or m/s) AVDEP = average water depth (ft or m) DB5O = median diameter of bed material (ft or m) AKAPPA = Karman constant (AKAPPA = 0.4)
The shear stress (TAU) on a lake bed is calculated as:
TAU = GAM*(USTAR**2)/GRAV (43)
where: TAU = bed shear stress (lb/ft2 or kg/m2) GAM = unit weight, or density, of water (62.4 lb/ft3 or 1000 kg/m3) GRAV = acceleration due to gravity (32.2 ft/sec2 or 9.8l m/sec2)
If the RCHRES being simulated is a stream or river, both shear velocity and shearstress are determined as functions of the slope and hydraulic radius of the reach:
USTAR = SQRT(GRAV*SLOPE*HRAD) (44)
where: SLOPE = slope of the RCHRES (-) HRAD = hydraulic radius (ft or m)
and
TAU = SLOPE*GAM*HRAD (45)
where: TAU = stream bed shear stress (lb/ft2 or kg/m2)
Module Section HYDR
150
Water Rights Categories
Categories can be used to facilitate the modeling of water rights in a RCHRES. Ifcategories are being simulated (NCAT > 0) in the CATEGORY block), each RCHRES inthe run keeps track of the categories of all inflows, storages, and outflows, aswell as precipitation and evaporation fluxes. Up to 100 categories may bespecified in the CATEGORY block.
The storage of each category of water in a RCHRES is called CVOL(C). The inflowsof each category are CIVOL(C). The category outflows from exit gate OFLO(N) arecalled COVOL(C,N), and the total outflow of each category is CROVOL(C). Thesequantities are illustrated in Figure 4.2(3).1-7.
The initial storage of water in a RCHRES may be assigned to a single category, orfractions of the storage can be assigned to specified categories. The default isto divide the storage equally among all active categories.
All water entering a RCHRES must be assigned a category. The inflow to eachcategory is input as time series CIVOL, and IVOL is computed as the sum.
By default, precipitation is divided proportionally among all categories presentin a RCHRES according to their current storage fraction CFRAC(C), which iscalculated as CVOL(C) divided by VOL. Optionally, it may be assigned to either asingle category or to several categories by user-defined fractions.
Assigning evaporation losses to categories is somewhat more complicated. Bydefault, evaporation is taken from all categories proportionally based on CFRAC.If a single category is specified, evaporation is taken from that category as longas sufficient water is present. For more complex situations, a priority may beassigned to each of several categories. Multiple categories may be given the samepriority, and losses may be divided among them by either user-specified fractionsor by CFRAC (the default). When all specified categories are exhausted, anyremaining loss is distributed among the other categories by CFRAC.
F(VOL) outflows are calculated from the FTABLE normally. Most free-flowing reacheswill use the default algorithm, which is to pass all categories downstreamunchanged, i.e. according to CFRAC. For more complex situations, categories canbe assigned in the same way as for evaporation, with separate priorities andcategories specified for each exit.
Time series demands (g(t) releases) are handled differently. The time seriesOUTDGT is replaced by COTDGT, which is an array specifying the demand from eachcategory for each exit. If there is not enough water in a category to satisfy thedemand, the release is cut back, and the storage of that category is reduced tozero. Water is not "borrowed" from other categories to make up the flow. Anydeficit in the demand is accumulated throughout the run in the time series CDFVOL.If a category is drawn from more than one exit, a priority may be established foreach demand. The priority may be specified as either a real number or a date, suchas an ownership date.
Module Section HYDR
151
Figure 4.2(3).1-7 Flow diagram for water categories in the HYDR Section of the RCHRES Application Module
Module Section HYDR
152
If an exit uses FUNCT to combine f(VOL) and g(t) demands, then OUTDGT is calculatedas the sum of COTDGT for that exit, and the combining function is applied normally.
COVOL is then the result of the actual releases of g(t) demands COTDGT and theapportionment of f(VOL) flows by category. The outflow rate is called CO(C,N).The total category outflow volume and rate are CROVOL and CRO, respectively.
Module Section ADCALC
153
4.2(3).2 Prepare to Simulate Advection of Fully Entrained Constituents (Section ADCALC of Module RCHRES)
Purpose
ADCALC calculates values for variables which are necessary to simulate longitudinaladvection of dissolved or entrained constituents. These variables are dependentupon the volume and outflow values calculated in the hydraulics section (HYDR).
Approach
The outflow of an entrained constituent is a weighted mean of two quantities: oneis an estimate based on conditions at the start of the time step, the otherreflects conditions at the end of the time step. The weighting factors are calledJS and COJS (complement of JS), respectively. The values of the weightingcoefficients depend on (1) the relative volume of stored water in the RCHREScompared to the volume leaving in a single time step and (2) the uniformity of thevelocity across a cross-section of the RCHRES. In order to represent thesefactors, two variables are defined: RAT and CRRAT. RAT is the ratio of RCHRESvolume at the start of the interval to the outflow volume based on the outflow rateat the start of the interval:
RAT = VOLS/(ROS*DELTS) (1)
where: VOLS = volume of water at the start of interval (ft3 or m3) ROS = outflow rate at start of interval (ft3/s or m3/s) DELTS = number of seconds in interval
The parameter CRRAT is defined as the ratio of maximum velocity to mean velocityin the RCHRES cross-section under typical flow conditions. CRRAT must always havea value of 1.0 or greater. A value of 1.0 corresponds to a totally uniformvelocity (plug flow) across the RCHRES.
Determination of JS and COJS
If the value of RAT is greater than that of CRRAT, it is assumed that all outflowover a given time interval was contained in the RCHRES at the start of theinterval, and the mean rate of outflow of material is entirely dependent upon therate of outflow at the start of the interval (JS = 1.0). If the value of RAT isless than CRRAT, it is assumed that part of the water in the outflow entered theRCHRES as inflow during the same interval; in this case, the concentration ofinflowing material will affect the outflow concentration in the same interval, andJS will have a value less than 1.0. The relationship of RAT, CRRAT, and JS isillustrated in Figure 4.2(3).2-1. COJS is (1.0 - JS).
Another way to interpret the relationship of these variables is that no inflowingmaterial is present in the outflow in the same interval if the outflow volume isless than (VOLS/CRRAT).
Module Section ADCALC
154
Figure 4.2(3).2-1 Determination of weighting factors for advection calculations
Module Section ADCALC
155
Calculation of Components of Outflow Volume
Components of outflow volume based on conditions at the start of the interval(SROVOL) and the end of the interval (EROVOL) are calculated as:
SROVOL = JS*ROS*DELTS (2) EROVOL = COJS*RO*DELTS
where: SROVOL = outflow volume component based on start of interval (ft3/interval or m3/interval) EROVOL = outflow volume component based on end of interval (ft3/interval or m3/interval) ROS = outflow rate at start of interval (ft3/s or m3/s) RO = outflow rate at end of interval (ft3/s or m3/s) DELTS = number of seconds in interval
Likewise, if there is more than one exit gate for the RCHRES, the correspondingoutflow components for each unit, based on conditions at the start and end of eachinterval, are calculated as:
SOVOL(N) = JS*OS(N)*DELTS (3) EOVOL(N) = COJS*O(N)*DELTS
where: SOVOL(N) = outflow volume component based on start of interval for exit gate N (ft3/interval or m3/interval) EOVOL(N) = outflow volume component based on end of interval for exit gate N (ft3/interval or m3/interval) OS(N) = outflow rate at start of interval for exit gate N (ft3/s or m3/s) O(N) = outflow rate at end of interval for exit gate N (ft3/s or m3/s) DELTS = number of seconds in interval
It should be noted that SROVOL, EROVOL, SOVOL(N), and EOVOL(N) are not actualoutflows from the RCHRES, but instead are components of outflow based on conditionsat the start or end of the interval. These variables are used in subroutine ADVECTto estimate the advection of constituents.
Module Section CONS
156
4.2(3).3 Simulate Conservative Constituents (Section CONS of Module RCHRES)
Purpose
CONS simulates constituents which, for all practical purposes, do not decay withtime or leave the RCHRES by any mechanism other than advection. Examples include:total dissolved solids, chlorides, and pesticides and herbicides which decay veryslowly. Figure 4.2(3).3-1 illustrates the fluxes of conservative material that aremodeled in CONS.
Method
Subroutine CONS performs three functions. First, a value for inflow of material(INCON) is obtained and converted to internal units. The inflow is the sum ofinputs from upstream reaches, tributary land areas, and atmospheric deposition:
INCON = ICON + SAREA*ADFX + SAREA*PREC*ADCN (1)
where: INCON = total input to reach (mass/interval) ICON = input from upstream reaches and tributary land (mass/interval) SAREA = surface area of reach (area) ADFX = dry or total atmospheric deposition flux (mass/area per interval) PREC = precipitation (depth) ADCN = concentration for wet atmospheric deposition in mass/volume
The atmospheric deposition inputs can be specified in two possible ways dependingon the form of the available data. If the deposition is in the form of a flux(mass per area per time), then it is considered "dry deposition". If thedeposition is in the form of a concentration in rainfall, then it is considered"wet deposition", and the program automatically combines it with the input rainfalltime series to compute the resulting flux. Either type of deposition data can beinput as a time series, or as a set of monthly values. The atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forRCHRES, and are specified in the EXT SOURCES block of the UCI. Monthly values areinput in the MONTH-DATA block.
After computing inflows, CONS calls subroutine ADVECT to perform longitudinaladvection of this material and the material already contained in the RCHRES.Finally, CONS calculates the mass of material remaining in the RCHRES afteradvection; this value, RCON, is necessary for the mass balance checks onconservatives and is calculated as:
RCON = CON*VOL (2)
where: RCON = mass of material in RCHRES after advection CON = concentration of conservative after advection VOL = volume of water in RCHRES at end of interval (ft3 or m3)
Module Section CONS
157
Figure 4.2(3).3-1 Flow diagram for conservative constituents in the CONS section of the RCHRES Application Module
Module Section CONS
158
Additional Requirements
HSPF allows a maximum of ten conservative constituents. The user selects the unitsfor each constituent; thus, different conservative constituents may have differentunits. However, in order to provide this flexibility, additional input isrequired. For each constituent the following information must be provided in theUser's Control Input:
1. CONID: the name of the constituent (up to 20 characters long)
2. QTYID: this string (up to 8 characters) contains the units used to describe the quantity of constituent entering or leaving the RCHRES, or the total quantity of material stored in it. Examples of possible units for QTYID are 'kg' for kilograms or 'lbs' for pounds
3. CONCID: the concentration units for each conservative (up to 8 characters long); examples are 'mg/l' or 'lbs/ft3'
4. CONV: conversion factor from QTYID/VOL to desired concentration units: CONC = CONV*(QTY/VOL) (in English system, VOL is expressed in ft3) (in metric system, VOL is expressed in m3) For example, if: CONCID is mg/l QTYID is kg VOL is in m3 then CONV = 1000.0
Module Section CONS
159
4.2(3).3.1 Simulate Advection of Constituent Totally Entrained in Water (subroutine ADVECT)
Purpose
ADVECT computes the concentration of material in a RCHRES and the quantities ofmaterial that leave the RCHRES due to longitudinal advection through active exits.ADVECT is a generalized subroutine, and is called by each module section whichsimulates constituents which undergo normal longitudinal advection.
Assumptions
Two assumptions are made in the solution technique for normal advection:
1. Each constituent advected by calling subroutine ADVECT is uniformly dispersedthroughout the waters of the RCHRES.
2. Each constituent is completely entrained by the flow; that is, the materialmoves at the same horizontal velocity as the water.
Method
The equation of continuity may be written as:
IMAT - ROMAT = (CONC*VOL) - (CONCS*VOLS) (2)
where: IMAT = inflow of material over the interval ROMAT = total outflow of material over the interval CONCS = concentration at the start of the interval CONC = concentration at the end of the interval VOLS = volume of water stored in the RCHRES at the start of the interval VOL = volume of water stored in the RCHRES at the end of the interval
The other basic equation states that the total outflow of material over the timeinterval is a weighted mean of two estimates; one based on conditions at the startof the interval, the other on ending conditions:
ROMAT = ((JS*ROS*CONCS) + (COJS*RO*CONC))*DELTS (3)
where: JS = weighting factor COJS = 1.0 - JS ROS = rates of outflow at the start of the interval (m3/s or ft3/s) RO = rates of outflow at the end of the interval (m3/s or ft3/s) DELTS = length of interval (seconds)
Using Equations (2) in Section 4.2(3).2 (Subroutine ADCALC), Equation (3) can bewritten:
ROMAT = (SROVOL*CONCS) + (EROVOL*CONC) (4)
Module Section CONS
160
where SROVOL and EROVOL are as defined earlier.
By combining Equations (2) and (4) we can solve for CONC:
CONC = (IMAT + CONCS*(VOLS - SROVOL))/(VOL + EROVOL) (5)
The total amount of material leaving the RCHRES during the interval is calculatedfrom equation (4).
If there is more than one active exit from the RCHRES, the amount of materialleaving through each exit is calculated as:
OMAT = SOVOL*CONCS + EOVOL*CONC (6)
where: OMAT = amount of material leaving RCHRES through individual exit SOVOL = outflow volume component for individual exit based on start of interval EOVOL = outflow volume component for individual exit based on end of interval
(SOVOL and EOVOL are defined in Section 4.2(3).2)
If the RCHRES goes dry during the interval, the concentration at the end of theinterval is undefined. The total amount of material leaving the RCHRES is:
ROMAT = IMAT + (CONCS*VOLS) (7)
If there is more than one active exit from the RCHRES, the amount of materialleaving through each exit from a RCHRES which has gone dry during the interval iscalculated as:
OMAT = (SOVOL/SROVOL)*ROMAT (8)
The units in the preceding equations are:
VOLS,VOL m3 or ft3 (call these volunits) SROVOL,etc volunits/interval CONCS,CONC user defined (call these concunits) IMAT,ROMAT,etc concunits * volunits/interval
Module Section HTRCH
161
4.2(3).4 Simulate Heat Exchange and Water Temperature (Section HTRCH of Module RCHRES)
Purpose
The purpose of this code is to simulate the processes which determine the watertemperature in a reach or mixed reservoir. Water temperature is one of the mostfundamental indices used to determine the nature of an aquatic environment. Mostprocesses of functional importance to an environment are affected by temperature.For example, the saturation level of dissolved oxygen varies inversely withtemperature. The decay of reduced organic matter, and hence oxygen demand causedby the decay, increases with increasing temperature. Some form of temperaturedependence is present in nearly all processes. The prevalence of individualphytoplankton and zooplankton species is often temperature dependent.
Required Time Series
Five time series of meteorological data are required to simulate the temperaturebalance within a RCHRES. These are:
1. solar radiation in langleys/interval 2. cloud cover expressed as tenths 3. air temperature in degrees F (English) or degrees C (Metric) 4. dewpoint temperature in degrees F (English) or degrees C (Metric) 5. wind speed in miles/interval (English) or km/interval (Metric)
Note that solar radiation data are usually available as daily totals. The usermust generally convert these data to hourly or two hourly values before using themin HSPF. If the standard HSPF disaggregation rule were used, a daily value wouldbe divided into equal increments for each interval of the day; this would notaccount for the rising and setting of the sun. A similar kind of preprocessingneeds to be done if daily max/min air temperatures are used.
Schematic View of Fluxes and Storages
Figure 4.2(3).4-1 illustrates the fluxes involved in this module section. Thereare no significant internal sources or sinks of temperature within a RCHRES.Changes in heat content are due only to transport processes across the RCHRESboundaries. Module section HTRCH considers three major processes: heat transferby advection, heat transfer across the air-water interface, and optionally, heattransfer across the water-sediment (bed) interface. The processes of diffusion anddispersion are not considered in HSPF.
Heat transfer by advection is simulated by treating water temperature as a thermalconcentration. This enables the use of subroutine ADVECT, a standard subroutinewhich calculates advective transport of constituents totally entrained in themoving water.
Module Section HTRCH
162
Figure 4.2(3).4-1 Flow diagram for HTRCH section of the RCHRES Application Module
Module Section HTRCH
163
Heat is transported across the air-water interface by a number of mechanisms, andeach must be evaluated individually. The net transport across the air-waterinterface is the sum of the individual effects. Mechanisms which can increase theheat content of the water are absorption of solar radiation, absorption of longwaveradiation, and conduction-convection. Mechanisms which decrease the heat contentare emission of longwave radiation, conduction-convection, and evaporation.
Shortwave Solar Radiation The shortwave radiation absorbed by a RCHRES is approximated by the followingequation: QSR = 0.97*CFSAEX*SOLRAD*10.0 (1) where: QSR = shortwave radiation (kcal/m2/interval) 0.97 = fraction of incident radiation which is assumed absorbed (3 percent is assumed reflected) CFSAEX = ratio of radiation incident to water surface to radiation incident to gage where data were collected. This factor also accounts for shading of the water body, e.g., by trees SOLRAD = solar radiation (langleys/interval) 10.0 = conversion factor from langleys to kcal/m2
Longwave Radiation All terrestrial surfaces, as well as the atmosphere, emit longwave radiation. Therate at which each source emits longwave radiation is dependent upon its temper-ature. The longwave radiation exchange between the atmosphere and the RCHRES isestimated using the formula: QB = SIGMA*((TWKELV**4) - KATRAD*(10**-6)*CLDFAC*(TAKELV**6))*DELT60 (2) where: QB = net transport of longwave radiation (kcal/m2/interval) SIGMA = Stephan-Boltzman constant multiplied by 0.97 to account for emissivity of water TWKELV = water temperature (degrees Kelvin) KATRAD = atmospheric longwave radiation coefficient with a typical value of 9.0 CLDFAC = 1.0 + (.0017*C**2) TAKELV = air temperature corrected for elevation difference (deg K) C = cloud cover, expressed as tenths (range = 0 - 10) DELT60 = DELT(minutes) divided by 60
Both atmospheric radiation to the water body and back radiation from the water bodyto the atmosphere are considered in this equation. QB is positive for transportof energy from the water body to the atmosphere.
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Conduction-Convection Conductive-convective transport of heat is caused by temperature differencesbetween the air and water. Heat is transported from the warmer medium to thecooler medium; heat can therefore enter or leave a water body, depending upon itstemperature relative to air temperature. HSPF assumes that the heat transport isproportional to the temperature difference between the two media. The equationused is: QH = CFPRES*(KCOND*10**-4)*WIND*(TW - AIRTMP) (3) where: QH = conductive-convective heat transport (kcal/m2/interval) CFPRES = pressure correction factor (dependent on elevation) KCOND = conductive-convective heat transport coefficient (typically in the range 1 - 20) WIND = wind speed (m/interval) TW = water temperature (deg C) AIRTMP = air temperature (deg C) QH is positive for heat transfer from the water to the air.
Evaporative Heat Loss Evaporative heat transport occurs when water evaporates from the water surface.The amount of heat lost depends on the latent heat of vaporization for water andon the quantity of water evaporated. For purposes of water temperature simulation,HSPF uses the following equation to calculate the amount of water evaporated:
EVAP = (KEVAP*10**-9)*WIND*(VPRESW - VPRESA) (4)
where: EVAP = quantity of water evaporated (m/interval) KEVAP = evaporation coefficient with typical values of 1 - 5 WIND = wind movement 2 m above the water surface (m/interval) VPRESW = saturation vapor pressure at the water surface (mbar) VPRESA = vapor pressure of air above water surface (mbar)
The heat removed by evaporation is then calculated: QE = HFACT*EVAP (5)
where: QE = heat loss due to evaporation (kcal/m2/interval) HFACT = heat loss conversion factor (latent heat of vaporization multiplied by density of water)
Module Section HTRCH
165
Heat Content of Precipitation In module section HYDR, an option exists to include the input of water fromprecipitation falling directly on the water surface. If this option is activated,it is necessary to assign a temperature to the water added to the RCHRES in thismanner. HSPF assumes that precipitation has the same temperature as the watersurface on which it falls.
Bed conduction
Heat movement between water and bed sediment contributes significantly to thediurnal variation of water temperature, especially in shallow streams and rivers.Simulation of bed conduction is optional, and the user may select from threealternative methods to represent this process.
Method 1:
If BEDFLG = 1, streambed conduction is computed as a simple function of thedifference in temperature between the water-streambed interface (temperature =water temperature) and the streambed at an equilibrium ground temperature at somedepth below the bed. The equation is:
QBED = KMUD * (TGRND - TW)
where:QBED = heat flux from ground to water (kcal/m2/interval)TGRND = equilibrium ground temperature (C)TW = water temperature (C)KMUD = water-ground heat conduction coefficient (kcal/m2/C/interval)
KMUD can be estimated as the thermal conductivity of the streambed material dividedby the depth (below the water-sediment interface) where equilibrium temperature isassumed to occur.
Method 2:
If BEDFLG = 2, bed conduction is based on the method of Caupp et al. (1994) thatwas used in modeling the Truckee River. The method is an extension of Method 1 toinclude a finite sediment or mud layer (consisting of water-saturated sediment)overlying the ground, which is at an equilibrium temperature. Heat fluxes betweenthe ground and sediment and between the sediment and water are computed, as wellas sediment and water temperatures. The algorithm, which includes a differencingscheme for updating the sediment temperature is described below:
The heat transfer between the ground and sediment is computed as follows:
QGRMUD = KGRND*(TGRND - TMUD)
Module Section HTRCH
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where:QGRMUD = heat transfer from ground to sediment layer (kcal/m2/interval)KGRND = ground-sediment heat conduction coefficient
(kcal/m2/C/interval); (default value = 1.419)TGRND = equilibrium ground temperature (C)TMUD = sediment temperature (C)
This heat transfer is used to update the sediment temperature as follows:
TMUD = TMUD + QGRMUD/CPR/MUDDEP
where:CPR = heat capacity of sediment (1000 kcal/m3/C)
(CPR is assumed to be the heat capacity of water) MUDDEP = depth of sediment layer (m)
Finally, the new sediment temperature is used to compute the heat transfer betweenthe sediment and water column:
QBED = KMUD*(TMUD - TW)
where:QBED = heat flux from sediment to water (kcal/m2/hr)KMUD = water-sediment heat conduction coefficient (kcal/m2/C/hr)TW = water temperature (C)
Method 3:
If BEDFLG = 3, the bed conduction computation is based on the method proposed byJobson (1977, 1979) in which the advection-dispersion equation for heat is solvedanalytically. Jobson's solution reduces the bed conduction to a convergent seriesconsisting of the product of the following quantities:
1. Temperature change (deg C) of the water over a specific period (TSTOPintervals) prior to the current time interval. ( )T)
2. Heat flux per degree C between the bed and water (over the TSTOP time period).The units are kcal/m2/C/interval. ()H)
Assuming time intervals of one hour, the equation is:
QBED = '(I=1,TSTOP) [DELH(I) * DELTT(I)]
where:'(I=1,TSTOP)= summation over the past TSTOP hoursDELH(I) = heat flux from bed to water at current interval resulting from
a 1.0 degree C temperature increase at hour I (kcal/m2/C/hr)DELTT(I) = temperature change over hour I
Module Section HTRCH
167
Therefore, at each time step, the total bed conduction flux is simply thesummation, over TSTOP, of the product of these two arrays. As implemented in HSPF,the temperature change at the current time step is computed twice. The firstcomputation includes all heat flux components except bed conduction; then theresulting temperature change is used to compute the bed conduction flux, which isused to compute the final water temperature change.
Values of DELH and TSTOP can be developed from Jobson's equations; a utilityprogram is available to compute these inputs as a function of the thickness andthermal properties of the bed. Note: The input values of DELH depend upon time-step, i.e., the units are kcal/m2/C/interval. Also, DELH values are negative.
Net heat exchange The net heat exchange at the water surface is represented as: QT = QSR - QB - QH - QE + QP + QBED (6) where: QT = net heat exchange in kcal/m2/interval QSR = net heat transport from incident shortwave radiation QB = net heat transport from longwave radiation QH = heat transport from conduction-convection QE = heat transport from evaporation QP = heat content of precipitation QBED= net heat exchange with bed
Calculation of Water Temperature Of the five heat transport mechanisms across the air-water interface, three aresignificant and dependent upon water temperature. In order to obtain a stablesolution for water temperature, these three terms (QB, QH, QE) are evaluated forthe temperature at both the start and end of the interval, and the average of thetwo values is taken (trapezoidal approximation). For this purpose, the unknownending temperature is approximated by performing a Taylor series expansion aboutthe starting temperature, and ignoring nonlinear terms. This formulation leads tothe following equation for the change in water temperature over the interval: DELTTW = CVQT*QT/(1.0 + SPD*CVQT) (7) where: DELTTW = change in water temperature (deg C) CVQT = conversion factor to convert total heat exchange expressed in kcal/m2/interval to deg C/interval (volume dependent) QT = net heat exchange in kcal/m2/interval (with terms evaluated at starting temperature) SPD = sum of partial derivatives of QB, QH, and QE with respect to water temperature
Module Section HTRCH
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The heat exchange calculations do not give realistic results when the water bodybecomes excessively shallow. Consequently, heat transport processes are notconsidered if the average depth of water in the RCHRES falls below 2 inches. Whenthis happens, the water temperature is set equal to the air temperature.
4.2(3).4.1 Correct Air Temperature for Elevation Difference (subroutine RATEMP)
Purpose
The purpose of this code is to correct air temperature for any elevation differencebetween the RCHRES and the temperature gage.
Approach
The lapse rate for air temperature is dependent upon whether or not precipitationoccurs during the time interval. If precipitation does occur, a wet lapse rate of1.94E-3 degrees C/ft is assumed. Otherwise, a dry lapse rate which is a functionof the time of day is used. A table of 24 hourly dry lapse rates is built intoHSPF. The corrected air temperature is:
AIRTMP = GATMP - LAPS*ELDAT (8)
where: AIRTMP = corrected air temperature (deg C) GATMP = air temperature at gage LAPS = lapse rate (degrees C/ft) ELDAT = elevation difference between mean RCHRES elevation and gage elevation (feet) (ELDAT is positive if the mean RCHRES elevation is greater than the gage elevation)
Module Section HTRCH
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4.2(3).5 Simulate Behavior of Inorganic Sediment (Section SEDTRN of Module RCHRES)
Purpose
The purpose of this code is to simulate the transport, deposition, and scour ofinorganic sediment in free-flowing reaches and mixed reservoirs. The modeling ofsediment in channels may be needed for analysis of such problems as:
1. Structural instability of bridge piers or water intakes caused by scouring.
2. Reduction of reservoir capacity and clogging of irrigation canals andnavigable waterways due to deposition.
3. Reduction of light available to aquatic organisms caused by suspendedsediment.
4. Transport of adsorbed pollutants such as fertilizers, herbicides, and
pesticides.
Schematic View of Fluxes and Storages Figure 4.2(3).5-1 shows the principal state variables and fluxes with which modulesection SEDTRN deals.
Both the migration characteristics and the adsorptive capacities of sediment varysignificantly with particle size. Consequently, HSPF divides the inorganicsediment load into three components (sand, silt, and clay), each with its ownproperties. Parametric information required for cohesive sediments (silt and clay)include: 1. particle diameter - D 2. particle settling velocity in still water - W 3. particle density - RHO 4. critical shear stress for deposition - TAUCD 5. critical shear stress for scour - TAUCS 6. erodibility coefficient - M Parameter values required for noncohesive, or sand, particles depend on the methodused to compute sandload (alternate methods are described in the functionaldescription of subroutines SANDLD, TOFFAL, and COLBY). If the Toffaleti method isused, values must be defined for median bed sediment diameter (DB5O) and particlesettling velocity (W). The Colby method requires a value for DB5O, and the powerfunction method requires both a coefficient (KSAND) for the power function and anexponent (EXPSND).
As Figure 4.2(3).5-1 indicates, the same material fluxes are modeled for all threefractions of sediment. Only the methodology used to determine fluxes betweensuspended storage and bed storage differ.
Module Section SEDTRN
170
Figure 4.2(3).5-1 Flow diagram of inorganic sediment fractions in the SEDTRN section of the RCHRES Application Module
Module Section SEDTRN
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HSPF assumes that scour or deposition of inorganic sediment does not affect thehydraulic properties of the channel. Furthermore, it is assumed that sand, silt,and clay deposit in different areas of the RCHRES bed; consequently, the depositionor scour of each material is not linked to the other fractions (i.e., "armoring"is not modeled). Longitudinal movement of bed sediments is not modeled.
The details of the transport, deposition, and scour techniques are outlined in thefunctional descriptions of the lower level routines of the SEDTRN module section.Following these calculations, the depth of sediment in the RCHRES bed is determinedin order to warn the user whenever the deposited sediment exceeds a pre-specifiedlevel. First, the volume occupied by each fraction of bed sediment is calculated: VOLSED(J) = RSED(J+3)/RHO(J)*1.0E06 (1)
where: VOLSED(J) = volume occupied by bed sediment of fraction J (m3 or ft3) RSED(J+3) = bed storage of sediment fraction J (mg.m3/l or mg.ft3/l) RHO(J) = particle density of fraction J (gm/cm3)
The volumes of the three fractions of bed sediment are summed, and the total bedvolume is adjusted to account for the fraction of the volume which is void ofsediment (i.e., the porosity):
VOLSEDA = VOLSED/(l.0 - POR) (2)
where: VOLSEDA = volume of bed adjusted to account for volume occupied by materials other than sediment VOLSED = volume of sediment contained in the bed (sand + silt + clay) POR = porosity of bed sediment (ratio of pore volume to total volume) Finally, the depth of bed sediment is calculated for use as an indicator ofexcessive deposition: BEDDEP = VOLSEDA/(LEN*BEDWID) (3) where: BEDDEP = depth of bed (m or ft) VOLSEDA = volume of bed (m3 or ft3) LEN = length of RCHRES (m or ft) BEDWID = effective width of bed for calculation of bed thickness (an input parameter expressed in m or ft)
If the calculated value for BEDDEP exceeds a user specified value, a warningmessage is printed to alert the user to potential modeling problems.
The PERLND module of HSPF simulates removal of total inorganic sediment due towashoff from the land surface and erosion from gullies. Therefore, the user mustdivide this total sediment into the three components (sand, silt, and clay) so thatthis material can be routed through the channel system in the RCHRES module.
Module Section SEDTRN
172
4.2(3).5.1 Simulate Cohesive Sediments (subroutine COHESV) Purpose COHESV simulates the deposition, scour, and transport processes of cohesivesediments (silt and clay). Method The modeling effort consists of two steps. First, subroutine ADVECT is called toperform advective transport (see section 4.2(3).3.1). Then subroutine BDEXCH iscalled, and deposition or scour is calculated based on the bed shear stress and theKrone and Partheniades equations. (see section 4.2(3).5.1.1).
4.2(3).5.1.1 Simulate Exchange with Bed (subroutine BDEXCH)
Purpose BDEXCH simulates the deposition and scour of cohesive sediment fractions (silt andclay). Approach
Exchange of cohesive sediments with the bed is dependent upon the shear stressexerted upon the bed surface. The shear stress within the RCHRES is calculated insubroutine SHEAR of the HYDR section. Whenever shear stress (TAU) in the RCHRESis less than the user-supplied critical shear stress for deposition (TAUCD),deposition occurs; whenever shear stress is greater than the user-supplied criticalshear stress for scour (TAUCS), scouring of cohesive bed sediments occurs. Therate of deposition for a particular fraction of cohesive sediment is based on asimplification of Krone's (1962) equation to the following form: D = W*CONC*(1.0 - TAU/TAUCD) (4) where: D = rate at which sediment fraction settles out of suspension (mass/len2.ivl) W = settling velocity for cohesive sediment fraction (len/ivl) CONC = concentration of suspended sediment fraction (mass/len3) TAU = shear stress (lb/ft2 or kg/m2) TAUCD = critical shear stress for deposition (lb/ft2 or kg/m2) The rate of change of suspended sediment fraction concentration in the RCHRES dueto deposition can be expressed as: d(CONC)/dt = -(D/AVDEPM) (5)
Module Section SEDTRN
173
where: AVDEPM = average depth of water in RCHRES (m) By substituting the expression for deposition rate (D) from Equation 4, thefollowing equation is obtained: d(CONC)/dt = -(W*CONC/AVDEPM)*(1 - TAU/TAUCD) (6) By integrating and rearranging this equation, a solution may be obtained for theconcentration of suspended sediment lost to deposition during a simulation interval(DEPCONC):
DEPCONC = CONC*(1.0 - EXP((-W/AVDEPM)*(1.0 - TAU/TAUCD)) (7)
where: CONC = concentration of suspended sediment at start of interval (mg/l) W = settling velocity for sediment fraction (m/ivl) AVDEPM = average depth of water in RCHRES in meters (calculated in HYDR) TAU = shear stress (lb/ft2 or kg/m2) TAUCD = critical shear stress for deposition (lb/ft2 or kg/m2)
The user must supply values for settling velocity (W) and critical shear stress fordeposition (TAUCD) for each fraction of cohesive sediment (silt and clay). Following the calculation of DEPCONC, the storage of sediment in suspension and inthe bed is updated:
SUSP = SUSP - (DEPCONC*VOL) (8) BED = BED + (DEPCONC*VOL) (9)
where: SUSP = suspended storage of sediment fraction (mg.ft3/l or mg.m3/l) BED = storage of sediment fraction in bed (mg.ft3/l or mg.m3/l) VOL = volume of water in RCHRES (ft3 or m3) The rate of resuspension, or scour, of cohesive sediments from the bed is derivedfrom a modified form of Partheniades'(1962) equation: S = M*(TAU/TAUCS - 1.0) (10)
where: S = rate at which sediment is scoured from the bed (mass/len2.ivl) M = erodibility coefficient for the sediment fraction (kg/m2.ivl) TAUCS = critical shear stress for scour (lbs/ft2 or kg/m2) The rate of change of suspended sediment fraction concentration in the RCHRES dueto scour can be expressed as: d(CONC)/dt = S/AVDEPM (11)
Module Section SEDTRN
174
By substituting the expression for scour rate (S) from Equation 10 the followingequation is obtained: d(CONC)/dt = (M/AVDEPM)*(TAU/TAUCS - 1.0) (12) By integrating and rearranging this equation, a solution may be obtained for theconcentration of suspended sediment added to suspension by scour during asimulation interval (SCRCONC): SCRCONC = M/AVDEPM*1000*(TAU/TAUCS - 1.0) (13) where: M = erodibility coefficient (kg/m2.ivl) AVDEPM = average depth of water (m) 1000 = conversion from kg/m3 to mg/l
The user is required to supply values for the erodibility coefficient (M) andcritical shear stress for scour (TAUCS) for each fraction of cohesive sediment(silt and clay) which is modeled.
Following the calculation of SCRCONC, the storage of sediment in suspension and inthe bed is updated: BED = BED - (SCRCONC*VOL) (14) SUSP = SUSP + (SCRCONC*VOL) (15) If the amount of scour calculated is greater than available storage in the bed, thebed scour is set equal to the bed storage, and the bed storage is set equal tozero. Since the value specified for TAUCS should be greater than that for TAUCD,only one process (deposition or scour) occurs during each simulation interval.
4.2(3).5.2 Simulate Behavior of Sand/Gravel (subroutine SANDLD)
Purpose SANDLD simulates the deposition, scour, and transport processes of the sandfraction of inorganic sediment. Method
Erosion and deposition of sand, or noncohesive sediment, is affected by the amountof sediment the flow is capable of carrying. If the amount of sand beingtransported is less than the flow can carry for the hydrodynamic conditions of theRCHRES, sand will be scoured from the bed. This occurs until the actual sandtransport rate becomes equal to the carrying capacity of the flow or until theavailable bed sand is all scoured. Conversely, deposition occurs if the sandtransport rate exceeds the flow's capacity to carry sand.
Module Section SEDTRN
175
Subroutine SANDLD allows the user to calculate sand transport capacity for a RCHRESby any one of three methods. Depending on the value of SANDFG specified in theUser's Control Input, either the Toffaleti equation (SANDFG=1), the Colby method(SANDFG=2), or an input power function of velocity (SANDFG=3) is used. If sandtransport capacity is calculated using the Toffaleti or Colby methods, thepotential sandload concentration is determined by the following conversion:
PSAND = (GSI*TWIDE*10.5)/ROM (16)
where: PSAND = potential sandload (mg/l) GSI = sand transport capacity (tons/day/ft of width) (calculated in COLBY or TOFFAL) TWIDE = width of RCHRES (ft) 10.5 = conversion factor ROM = total rate of outflow of water from the RCHRES (m3/sec)
If carrying capacity is a power function of velocity, PSAND is calculated as:
PSAND = KSAND*AVVELE**EXPSND (17) where: KSAND = coefficient in the sandload suspension equation (input parameter) EXPSND = exponent in sandload suspension equation (input parameter) AVVELE = average velocity (ft/sec)
The potential outflow of sand during the interval is: PROSND = (SANDS*SROVOL) + (PSAND*EROVOL) (18)
where: PROSND = potential sand outflow SANDS = concentration of sand at start of interval (mg/1) SROVOL and EROVOL are as defined in Section 4.2(3).2 The potential scour from, or deposition to, the bed storage is found using thecontinuity equation: PSCOUR = (VOL*PSAND) - (VOLS*SANDS) + PROSND - ISAND (19)
where: PSCOUR = potential scour (+) or deposition (-) VOL = volume of water in RCHRES at the end of the interval (ft3 or m3) VOLS = volume of water in RCHRES at the start of interval (ft3 or m3) ISAND = total inflow of sand into RCHRES during interval
Module Section SEDTRN
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The terms in Equations 18 and 19 have the units of concentration. The potentialscour is compared to the amount of sand material on the bottom surface availablefor resuspension. If scour demand is less than available bottom sands, the demandis satisfied in full, and the bed storage is adjusted accordingly. The newsuspended concentration is PSAND. If the potential scour cannot be satisfied bybed storage, all of the available bed sand is suspended, and bed storage isexhausted. The concentration of suspended sandload is calculated as:
SAND = (ISAND + SCOUR + SANDS*(VOLS - SROVOL))/(VOL + EROVOL) (20)
where: SAND = concentration of sand at end of interval SCOUR = sand scoured from, or deposited to, the bottom SANDS = concentration of sand at start of interval The total amount of sand leaving the RCHRES during the interval is: ROSAND = SROVOL*SANDS + EROVOL*SAND (21) If a RCHRES goes dry during an interval, or if there is no outflow from the RCHRES,all the sand in suspension at the beginning of the interval is assumed to settleout, and the bed storage is correspondingly increased.
4.2(3).5.2.1 Calculate Sand Transport Capacity by Using Toffaleti's Method (subroutine TOFFAL)
Purpose TOFFAL uses Toffaleti's method to calculate the capacity of the RCHRES flow totransport sand.
Method In Toffaleti's methodology, the actual stream for which the sand discharge is tobe calculated is assumed to be equivalent to a two-dimensional stream of widthequal to that of the real stream and of depth equal to the hydraulic radius of thereal stream (FHRAD).
For the purposes of calculation, the depth, FHRAD, of the hypothetical stream isdivided into four zones shown in Figure 4.2(3).5-2. These are: (1) the bed zoneof relative thickness Y/FHRAD = 2*FDIAM/FHRAD; (2) the lower zone extending fromY/FHRAD = 2*FDIAM/FHRAD to Y/FHRAD = 1/11.24; (3) the middle zone extending fromY/FHRAD = 1/11.24 to Y/FHRAD = 1/2.5; and (4) the upper zone extending from Y/FHRAD= 1/2.5 to the surface. (FDIAM is the median bed sediment diameter). The velocityprofile is represented by the power relation:
U = (1 + CNV)*V*(Y/FHRAD)**CNV (22)
Module Section SEDTRN
177
Figure 4.2(3).5-2 Toffaleti's velocity, concentration, and sediment discharge relations
Module Section SEDTRN
178
where: U = flow velocity at distance Y above the bed (ft/sec) V = mean stream velocity (ft/sec) CNV = exponent derived empirically as a function of water temperature (0.1198 + 0.00048*TMPR) TMPR = water temperature (degrees F) The concentration distribution of sand is given by a power relation for each of thethree upper zones; i.e., by Equations 23-25 in Figure 4.2(3).5-2. The exponent,ZI, in Equations 23-25 is given by:
ZI = (VSET*V)/(CZ*FHRAD*SLOPE) (26)
where: VSET = settling velocity for sand (ft/s) SLOPE = slope of RCHRES (ft/ft) CZ = empirical factor derived as a function of water temperature (260.67 - 0.667*TMPR)
Expressions for the sand transport capacity of the lower (GSL), middle (GSM), andupper (GSU) zones are obtained by substituting U from Equation 22 and theappropriate value for sand particle concentration (CI) for each zone into thefollowing equation and integrating between the vertical limits of the zone:
GSI = INT [LLI to ULI] (CI*Udy) (27)
where: GSI = sand transport capacity for zone I INT = integral of function in ( ) over limits in [ ] ULI = depth Y at upper limit of zone I LLI = depth Y at lower limit of zone I CI = concentration of sand in zone I
The resulting equations for sand transport capacity in the three zones are:
GSL = CMI*(((HRAD/11.24)**(1.0 + CNV - 0.758*ZI) - (28) (2*FDIAM)**(1.0 + CNV - 0.756*ZI))/(1.0 + CNV - 0.756*ZI)) GSM = CMI*(((HRAD/11.24)**(0.244*ZI)*((HRAD/2.5)**(1.0 + CNV - ZI) - (HRAD/11.24)**(1.0 + CNV - ZI)))/(1.O + CNV - ZI)) (29)
GSU = CMI*(((HRAD/11.24)**(0.244*ZI)*(HRAD/2.5)**(0.5*ZI)* (30) (HRAD**(1.0 + CNV - 1.5*ZI) - (HRAD/2.5)**(1.0 + CNV - 1.5*ZI)))/ (1.0 + CNV - 1.5*ZI)) in which
CMI = 43.2*CLI*(1.0 + CNV)*V*HRAD**(0.758*ZI - CNV) (31)
Module Section SEDTRN
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A value for CLI, the concentration of sand in the lower zone, can be obtained bysetting the expression for GSL in Equation 28 equal to the following empiricalexpression and solving for CLI:
GSL = 0.6/((TT*AC*K4/V**2)**(1.67)*ããDIAM/0.00058)**(1.67)) (32)
where: GSL = sand transport capacity TT = empirical factor derived as a function of water temperature (1.10*(0.051 + 0.00009*TMPR)) AC = empirical factor derived as a function of the kinematic viscosity of water (VIS) and shear velocity based on shear stress due to sand grain roughness (USTAR) K4 = empirical factor derived as a function of AC, slope of the RCHRES (SLOPE), and particle diameter for which 65% by weight of sediment is finer (D65). V = mean stream velocity (ft/sec) FDIAM = median bed sediment diameter (ft)
Values for factors AC and K4 are given in Figure 4.2(3).5-3. The dimensions of ACare such that GSL is expressed in tons per day per foot of width. Consequently,when CLI is evaluated and substituted back into Equations 28-30 the resulting unitsof sand transport capacity for all three zones are tons per day per foot of width.
Prior to calculation of sand transport capacity for the zones, Equation 25 issolved to be sure that the value for concentration at Y=2*FDIAM does not exceed 100lbs/ft3. If it does, the concentration at this depth is set equal to 100 lbs/ft3and an adjusted value of CLI is calculated and used in Equations 28-30. Thetransport capacity of the final zone, the bed zone (Figure 4.2(3).5-2), is alsodetermined using the adjusted value of CLI and the following equation:
GSB = CMI*(2*FDIAM)**(1.0 + CNV - 0.758*ZI) (33)
The total sand transport capacity (GSI) for the RCHRES is the sum of the transportcapacities for the four zones:
GSI = GSB + GSL + GSM + GSU (34)
Module Section SEDTRN
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Figure 4.2(3).5-3 Factors in Toffaleti relations
Module Section SEDTRN
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4.2(3).5.2.2 Calculate Sand Transport Capacity by Using Colby's Method (subroutine COLBY)
Purpose
COLBY calculates the capacity of the RCHRES to transport sand based on the medianbed sediment diameter (DB50), average stream velocity (V), hydraulic radius (HRAD),fine sediment load concentration (FSL), and water temperature (TEMPR).
Method
The solution technique used in this subroutine is based on empirical relationshipsdeveloped from Figures 4.2(3).5-4 and 4.2(3).5-5. In general terms, the solutionconsists of three operations:
1. Obtain one value for sediment transport capacity from a matrix of values byinterpolation. The dimensions of the matrix (G) are 4x8x6 and correspond toranges of hydraulic radius, velocity, and mean diameter of bed sediment,respectively. Since Colby's curves were developed on a log-log scale, it isnecessary to perform a series of three linear interpolations of logarithmicvalues to derive the value for sediment transport appropriate for thehydraulic parameters in the RCHRES. This value (GTUC) is not corrected forthe effects of fine sediment concentration or water temperature.
2. Correct sand transport capacity value to account for water temperature inRCHRES. A multiplier is obtained from a matrix of values by interpolation.The dimensions of the matrix (T) are 7x4 and correspond to ranges of watertemperature and hydraulic radius, respectively. A linear interpolation oflogarithmic values is performed to derive the appropriate temperaturecorrection factor. Generally speaking sand transport capacity, measured intons per day per foot of stream width, decreases with increasing stream width(see Figure 4.2(3).5-5).
3. Correct sand transport capacity value to account for fine sediment load inRCHRES. A multiplier is obtained from a matrix of values by interpolation.The dimensions of the matrix (F) are 5x9 and correspond to ranges of finesediment load concentration and hydraulic radius, respectively. Again, alinear interpolation of logarithmic values is performed to derive theappropriate correction factor. Sand transport capacity increases withincreasing fine sediment load and with increasing stream width (Figure4.2(3).5-5). It should be noted, however, that the correction factor is notlarge for typical stream conditions. For example, the multipliercorresponding to a fine sediment load of 10,000 ppm (with hydraulic radius of1 foot) is 1.17.
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Figure 4.2(3).5-4 Colby's relationship for discharge of sands in terms of mean velocity for six median sizes of bed sands, four depths of flow, and water temperature of 60 F
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Figure 4.2(3).5-5 Colby's correction factors for effect of water temperature, concentration of fine sediment, and sediment size; applied to uncorrected discharge of sand given by Figure 4.2(3).5-4
Module Section SEDTRN
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The following additional comments are important to understanding and using theCOLBY subroutine in HSPF: 1. Fine sediment load is defined as the sum of suspended silt and clay.
2. If the value for median bed sediment diameter, hydraulic radius, or averagevelocity for the RCHRES for a given simulation interval falls outside therange of values considered in Colby's graphs, a solution for sand transportcapacity cannot be obtained by the Colby method. In this case, an errormessage is printed which specifies which parameter is out of range, andsubroutine TOFFAL is automatically called to obtain a solution using theToffaleti method.
Acceptable ranges of parameter values for COLBY are: (a) median bed sediment diameter 0.1-0.8 mm (b) hydraulic radius 0.1-100 ft (c) average velocity 1.0-10.0 ft/s
3. Both the Colby and Toffaleti formulations equate depth of flow to hydraulicradius. This approximation is best for wide rivers. Subroutines COLBY andTOFFAL were obtained and modified from Battelle Northwest Laboratories'SERATRA model (Onishi and Wise, 1979). In this model, the depth of flowvalues in Figures 4.2(3).5-4 and 4.2(3).5-5 are equated to hydraulic radiusvalues, and the HSPF version of COLBY has done the same. To the best of ourknowledge the accuracy of this approximation for narrow streams has not beendocumented.
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4.2(3).6 Simulate the Behavior of a Generalized Quality Constituent (Module Section GQUAL)
Purpose
The purpose of this code is to enable the model user to simulate the behavior ofa generalized constituent. The constituent which is modeled may be present in theRCHRES only in a dissolved state, or it may also be sediment-associated. If thegeneralized quality constituent, which will be called a "qual" throughout thisdiscussion, is not associated with sediment, module section GQUAL only considersthe following processes: 1. Advection of dissolved material
2. Decay processes. One or more of the following can be modeled: a. hydrolysis b. oxidation by free radical oxygen c. photolysis d. volatilization e. biodegradation f. generalized first-order decay
3. Production of one generalized quality constituent as a result of decay ofanother generalized quality constituent by any of the listed decay processesexcept volatilization. This capability is included to allow for situationsin which the decay products of a chemical are of primary interest to the user.
The following additional processes are considered if the generalized qualityconstituent being modeled is sediment-associated:
4. Advection of adsorbed suspended material
5. Deposition and scour of adsorbed material with sediment
6. Decay of suspended and bed material 7. Adsorption/desorption between dissolved and sediment-associated phase.
Schematic View of Fluxes and Storage
Figure 4.2(3).6-1 illustrates the fluxes and storages modeled in section GQUAL.Note that the arrows indicating fluxes from each of the sediment fraction storagesare not all labeled. For instance, although deposition and scour transfermaterials between the suspended storage and bed storage of all three sedimentfractions (sand, silt, clay), only the flux arrow for deposition/scour of clay islabeled. Deposition/scour flux arrows for sand and silt are left unlabeled so thatthe flow diagram does not become overly cluttered and incomprehensible. The sameconvention is used for the other fluxes contained in the flow diagram (i.e., anunlabeled flux arrow indicates that a flux of the same nature as a parallel labeledflux occurs).
Module Section GQUAL
186
Figure 4.2(3).6-1 Flow diagram for generalized quality constituent in the GQUAL section of the RCHRES Application Module
Module Section GQUAL
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Approach
The first portion of GQUAL evaluates the nature of the data which will be used forthe GQUAL simulation. Since it is anticipated that some users of section GQUALwill be using this section independently of many of the other sections of theRCHRES application module, a variety of data types are allowed. In particular,most data required for simulation of individual decay processes can be supplied inthe form of a single constant, 12 monthly constants, a time series value from theINPAD, or in cases where the data value is calculated in another active section ofRCHRES, the last computed value may be used. Data types which may be obtained fromany one of these sources include:
1. water temperature 2. pH (for hydrolysis) 3. free radical oxygen (for oxidation) 4. total suspended sediment (for photolysis) 5. phytoplankton (for photolysis) 6. cloud cover (for photolysis) 7. wind (for volatilization on lakes)
GQUAL utilizes six routines to perform the simulation of a generalized qualityconstituent. These six routines and their functions are:
1. OXREA: compute oxygen reaeration rate (used to simulate volatilization) 2. ADVECT: simulate advection of dissolved material 3. DDECAY: simulate decay of dissolved material 4. ADVQAL: advect sediment-associated material 5. ADECAY: simulate decay of qual adsorbed to suspended and bed sediment 6. ADSDES: simulate exchange of materials due to adsorption and desorption
Details on the methods used by these routines are provided in functionaldescriptions 4.2(3)3.1, 4.2(3).7.1.2, and 4.2(3).6.1 through 4.2(3).6.4,respectively.
Before ADVECT is called, GQUAL sums the inputs of dissolved qual from upstreamreaches, tributary land areas, and atmospheric deposition:
INDQAL = IDQAL + SAREA*ADFX + SAREA*PREC*ADCN (1)
where: INDQAL = total input of dissolved qual to reach IDQAL = input of dissolved qual from upstream reaches and tributary land SAREA = surface area of reach ADFX = dry or total atmospheric deposition flux in mass/area per interval PREC = precipitation depth ADCN = concentration for wet atmospheric deposition in mass/volume
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Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forRCHRES, and are specified in the EXT SOURCES block of the UCI. The monthly valuesare input in the MONTH-DATA block in the UCI.
GQUAL is also responsible for the calculation of increases in qual resulting fromdecay of a "parent" chemical. The HSPF code is designed so that a user may specifythat a "daughter" chemical is produced by any or all of the six decay processes(except volatilization) which degrade a parent qual. However, certain restrictionsare placed on the daughter/parent relationship. Simulation of up to threegeneralized quality constituents is allowed. Qual #2 may be produced by decay ofqual #1. Qual #3 may be produced by decay of qual #1 and/or qual #2. Otherrelationships are not allowed. The user should sequence quality constituentsaccordingly. The amount of daughter qual produced by decay of a parent by aparticular decay process is computed as:
PDQAL(I) = DDQAL(K,J)*C(I,J,K) (1)
where: PDQAL(I) = amount of daughter qual I produced by decay of parent qual J through process K (concu/l)*(ft3/ivl) or (concu/l)*(m3/ivl) DDQAL(K,J) = amount of parent material decayed by process K expressed in same units as PDQAL(I) C(I,J,K) = amount of qual I produced per unit of qual J degraded by process K in units of concu I/concu J After the amount of decay resulting from all active decay processes and the amountof input of qual produced by decay of parent qual(s) have been calculated, the newdissolved concentration of a qual is computed as:
DQAL(I) = DQAL(I) + (PDQAL(I) - DDQAL(7,I))/VOL (2)
where: DQAL(I) = concentration of dissolved qual I PDQAL(I) = amount of qual I produced by decay of parent qual(s) DDQAL(7,I) = total amount of qual I degraded by the decay processes VOL = volume of water in the RCHRES
Module Section GQUAL
189
Additional Requirements HSPF allows a maximum of 3 general quality constituents. The user selects theunits for each constituent; thus, different constituents may have different units.For example, the user may simulate fecal and total coliforms expressed in organismsper ml and a pesticide expressed in milligrams per liter in the same simulation.In order to provide this flexibility, additional input is required. For eachconstituent the following information must be provided in the User's Control Input:
GQID: the name of the constituent (up to 20 characters long) QTYID: this string (up to 8 characters) contains the units used to describe the
quantity of constituent entering or leaving the RCHRES, and the totalquantity of material stored in it. Examples of possible units for QTYIDare 'Morg' for millions of organisms or 'lbs' for pounds
CONCID: the concentration units for each decay constituent (up to 4 characters
long); examples are '#' or 'mg'. It is implied that these units are "perl".
CONV: conversion factor from QTYID/VOL to desired concentration units: CONC =CONV*(QTY/VOL) (in English system, VOL is expressed in ft3; in metricsystem, VOL is expressed in m3)
For example, if: CONCID is mg/l, QTYID is kg, and VOL is m3, then CONV = 1000.
4.2(3).6.1 Simulate Decay of Dissolved Material (subroutine DDECAY)
Purpose
DDECAY simulates the degradation of generalized quality constituents by chemicaland/or biological means. Six processes are considered:
1. hydrolysis 2. oxidation by free radical oxygen 3. photolysis 4. volatilization 5. biodegradation 6. generalized first-order decay
Module Section GQUAL
190
Discussion
HSPF includes detailed degradation methods only for the dissolved state of thequality constituent (qual); decay of qual material in the adsorbed state is handledby a lumped first-order decay function in subroutine ADECAY. Formulations of thedegradation processes are based on studies conducted by Smith et al. (1977, 1979),Zepp and Cline (1977), Falco et al. (1976), and Mill et al. (1980). Mostformulations are similar to those included in the SERATRA model (Onishi and Wise,1979). All degradation processes modeled in DDECAY contain a temperaturecorrection factor.
Methods
Hydrolysis
Hydrolysis is defined as any reaction that takes place in water, without the aidof light or microorganisms, in which a compound is transformed to a differentcompound as a result of reaction with water. The rate of change of dissolved qualconcentration due to hydrolysis is sensitive to changes in pH and watertemperature. In HSPF, the equation presented by Smith et al. (1977) is modifiedto include a temperature correction factor and rewritten as:
KHYD = (KA*10.0**(-PHVAL) + KB*10.0**(PHVAL - 14.0) + KN)* (3) THHYD**TW20
where: KHYD = hydrolysis rate constant for qual adjusted for pH and water temperature conditions of RCHRES KA = hydrolysis rate coefficient for qual in acidic solution (pH5) KB = hydrolysis rate coefficient for qual in basic solution (pH9) KN = hydrolysis rate coefficient for qual in neutral solution (pH7) PHVAL = pH of water in RCHRES THHYD = temperature correction parameter for hydrolysis TW20 = TW (water temperature in degrees C) - 20.0
The hydrolysis rate coefficients (KA, KB, KN) for a particular qual are determinedby standard laboratory tests (ASTM, 1980). Depending on the availability of dataand the needs of the model user, pH information for the hydrolysis equation can besupplied as (1) one constant value, (2) twelve monthly values, or (3) a timeseries. The time series can either be an input time series or the results of pHsimulation if section RQUAL is active and pH is simulated.
Oxidation by Free Radical Oxygen
Two general types of oxidation reactions can be distinguished for evaluatingchemical oxidation processes in an aquatic environment (Mill et al., 1980):
1. Reaction of an excited state of a molecule with oxygen, in which the excitedstate is produced by direct photolysis or by interaction with aphotosensitizer; this process is termed photo-oxidation.
Module Section GQUAL
191
2. Reaction of the ground state of the chemical with oxidants in solution, inwhich the oxidants are formed by reactions of dissolved or suspended naturalmaterials in solution; these reactions are termed thermal oxidation,autoxidation, or simply oxidation. The ultimate driving force for oxidantformation may, however, often be photochemical reactions of the naturalmaterials.
In HSPF, photo-oxidation is considered as one of the photo-initiated degradationprocesses collectively labelled as photolysis. Consequently, only thermaloxidation is considered in the following formulation. The rate of oxidation ofdissolved qual is expressed as a function of free radical oxygen concentration(ROC) and water temperature:
KROX = KOX*ROC*(THOX**TW20) (4)
where: KROX = oxidation rate constant for qual adjusted for free radical oxygen concentration and water temperature KOX = base oxidation rate coefficient for qual ROC = free radical oxygen concentration (moles/l (M)) THOX = temperature correction parameter for oxidation TW20 = TW (water temperature in degrees C) - 20.0
The oxidation rate coefficient (KOX) for a qual is determined from laboratorytests. Mill et al. (1980) cites two groups of oxidants which are likely to beimportant in natural waters: alkylperoxy radicals and singlet molecular oxygen.The overall free radical oxygen concentration can be specified by the user as aconstant value, twelve monthly values, or a time series.
Photolysis
Photochemical transformation of chemicals can occur when energy in the form oflight is absorbed by a molecule, placing it in an excited state from which reactioncan occur. Direct photolysis of chemicals occurs when the chemical molecule itselfabsorbs light and undergoes reaction from its excited state. Indirect photolysisoccurs when another chemical species, called a sensitizer, absorbs light and thesensitizer transfers energy from its excited state to another chemical, which thenundergoes reaction. There are many types of photochemical reactions, includingoxidation, reduction, hydrolysis, substitution, and rearrangement. In practice itis possible to measure the rate constant for photochemical reaction or a reactionquantum yield without knowing the types of reactions which are occurring (Mill etal., 1980). The formulation of photolysis developed for HSPF is intended tomeasure the net degradation of a generalized quality constituent which results fromphotochemical reactions.
The basic equation for rate of loss of a qual in dilute solution in an environ-mental water body due to absorbance of light of wavelength lambda is given by:
KPHOL = ((PHI*INLITL)/DEP)*FSLAM*FQLAM (5)
Module Section GQUAL
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where: KPHOL = rate of loss of qual due to photolysis by light of wavelength lambda PHI = reaction quantum yield for photolysis of qual (moles/einstein) INLITL = incident light intensity of wavelength lambda (einsteins/cm2.day) DEP = depth of water FSLAM = fraction of light absorbed by the system FQLAM = fraction of absorbed light that is absorbed by qual
The solution technique outlined by Mill and implemented in HSPF uses seasonalday-averaged, 24-hour light intensity values (LLAM) for 18 wavelength intervalsfrom 300 nm to 800 nm. In order to use these values, the relationship between thelight intensity variable (INLITL) in Equation 5 and the tabulated values for LLAMmust be defined. The relationship derived by Mill for relatively clear water orshallow depths can be written as:
INLITL = LLAM/2.3*BETA (6)
where: BETA = LLIT/DEP LLIT = path length of light through water DEP = depth of water
Further, the effects of cloud cover on light intensity are introduced by addingfactor CLDLAM:
INLITL = (LLAM/2.3*BETA)*CLDLAM (7)
where: CLDLAM = fraction of total light intensity of wavelength lambda which is not absorbed or scattered by clouds
CLDLAM is calculated as:
CLDLAM = (10.0 - CC*KCLDL)/10.0 (8)
where: CC = cloud cover in tenths KCLDL = efficiency of cloud cover in intercepting light of wavelength lambda, a user supplied parameter (default value 0.0)
By substitution of Equation 7 into Equation 5, the general equation for thephotolysis rate of a qual due to absorbance of light of wavelength lambda can beexpressed as:
KPHOL = ((PHI*LLAM*CLDLAM)/2.3*BETA*DEP)*FSLAM*FQLAM (9)
The general mathematical expression for the fraction of light absorbed by the watersystem (FSLAM) is:
FSLAM = 1.0 - 10**(-KLAM*LLIT) (10)
Module Section GQUAL
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The exponential coefficient, KLAM, in this equation has two components forlaboratory conditions:
KLAM = ALPHL + EPSLAM*C (11)
where: ALPHL = base absorbance term for light of wavelength lambda for the system (/cm) EPSLAM = absorbance term for light of wavelength lambda absorbed by qual (l/mole.cm) C = concentration of qual (moles/l)
For environmental systems, the effects of light absorbance by suspended sedimentand phytoplankton are introduced to the formulation, and KLAM is expanded to:
KLAM = ALPHL + EPSLAM*C + GAMLAM*SED + DELLAM*PHYTO (12)
where: GAMLAM = absorbance term for light absorbed by suspended sediment (l/mg/cm) SED = total suspended sediment (mg/l) DELLAM = absorbance term for light absorbed by phytoplankton (l/mg/cm) PHYTO = phytoplankton concentration (mg/l)
Because the concentration of qual is assumed small, the fraction of totalabsorbance of light in the water system resulting from absorbance by the qual isassumed negligible, and the term (EPSLAM*C) is dropped from Equation 12. Bysubstituting the modified value of KLAM into Equation 9 , setting LLIT = BETA*DEP(from Equation 6), and assuming that BETA = 1.2 (Mill, 1980), the final form of theexpression for FSLAM is obtained:
FSLAM = 1.0 - 10**(-1.2*KLAM*DEP) (13)
The remaining term of the general equation for photolysis (Equation 9) which mustbe evaluated is FQLAM, the fraction of total absorbed light that is absorbed by thequal. This term is evaluated as:
FQLAM = (EPSLAM*C)/KLAM (14)
Equation 9 can be rewritten as:
PHOFXL = ((PHI*LLAM*CLDLAM)/2.3*BETA*DEP)* (15) (1.0 - 10**(-1.2*KLAM*DEP))*(EPSLAM*C/KLAM)
To obtain the rate of loss of qual due to photolysis from absorption of light ofall wavelength intervals, Equation 15 must be summed over LLAM:
KPHO = (PHI/(2.76*DEP))*(SUM [1 to 18] ((LLAM* (16) CLDLAM*EPSLAM/KLAM)*(1.0 - EXP(-2.76*KLAM*DEP))
Module Section GQUAL
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The equation for the degradation rate due to photolysis used in HSPF is furthercomplicated by correction factors for surface shading and water temperature. Thefinal rearranged and expanded formulation is:
KPHO = (CF*DELT60/24.)*PHI*(SUM [1 to 18] ((LLAM*CLDLAM/2.76* (17) KLAM*DEP)*(1.0 - EXP(-2.76*KLAM*DEP))*EPSLAM))*THPHO**TW20
where: SUM = summation of function in ( ) over limits in [ ] CF = factor accounting for surface shading DELT60/24 = conversion from day to interval THPHO = temperature correction parameter for photolysis TW20 = TW (water temperature in degrees C) - 20.0
For simulation intervals of less than 24 hours, photolysis is assumed to occur onlybetween 6:00 AM and 6:00 PM during approximate daylight hours. In order to obtaina solution which is reasonably consistent with the input seasonal, day-averaged,24-hour light intensity values, the daily light intensity is assumed to beuniformly distributed over the 12 hours from 6:00 AM to 6:00 PM. Consequently,calculated photolysis rates are doubled during daylight hours and set equal to zerofor non-daylight hours. It should be noted that five look-up tables for solarintensity values (LLAM) are incorporated into HSPF. Tables 4.2(3).6-l through4.2(3).6-5 show the values for seasonal day-averaged, 24 hour light intensity at1O, 20, 30, 40, and 50 degrees latitude. The Run Interpreter checks the inputlatitude for the study area and selects the appropriate table from which to extractvalues. Additional input required to simulate photolysis in subroutine DDECAYinclude:
1. Molar absorption coefficients for each of the 18 wavelengths 2. Reaction quantum yield for qual (PHI) 3. Temperature correction parameter for photolysis (THPHO) 4. 18 values for base absorbance term for water system (ALPHL) 5. 18 values for absorbance for light absorbed by suspended sediment (GAMLAM) 6. 18 values for absorbance for light absorbed by phytoplankton (DELLAM) 7. Cloud cover values. Either a time series or 12 monthly values may be supplied. 8. Total suspended sediment values. Either a time series or 12 monthly values. 9. Phytoplankton values. Either a time series or 12 monthly.
Module Section GQUAL
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Table 4.2(3).6-1 Solar Intensity Values for Latitude 10 N
Wavelength, Solar Intensity, milli-einsteins/cm2/day Nanometers Spring Summer Fall Winter 300 1.02E-2 4.66E-4 4.19E-4 3.20E-4 303.75 1.78E-2 3.16E-3 2.87E-3 2.39E-3 308.75 2.85E-2 9.37E-3 8.51E-3 7.26E-3 313.75 3.27E-2 1.90E-2 1.73E-3 1.51E-2 318.75 4.18E-2 2.91E-2 2.66E-2 2.38E-2 323.1 3.70E-2 2.65E-2 2.91E-2 2.36E-2 346 3.39E-1 3.29E-1 2.99E-1 2.92E-1 370 4.33E-1 4.38E-1 3.85E-1 3.44E-1 400 8.40E-1 8.37E-1 7.64E-1 6.96E-1 430 1.16 1.17 1.07 9.80E-1 460 1.47 1.47 1.36 1.23 490 1.50 1.50 1.37 1.27 536.25 2.74 2.69 2.46 2.26 587.5 2.90 2.79 2.52 2.35 637.5 2.90 2.80 2.60 2.43 687.5 2.80 2.80 2.60 2.30 756 2.70 2.70 2.50 2.40 800 3.00 2.50 2.30 2.10
Table 4.2(3).6-2 Solar Intensity Values for Latitude 20 N
Wavelength, Solar Intensity, milli-einsteins/cm2/day Nanometers Spring Summer Fall Winter 300 3.51E-4 4.44E-4 2.74E-4 1.47E-4 303.75 2.51E-3 3.15E-3 2.20E-3 1.47E-3 308.75 8.09E-3 9.61E-3 6.89E-3 5.34E-3 313.75 1.81E-2 1.97E-2 1.48E-2 1.15E-2 318.75 2.82E-2 3.02E-2 2.33E-2 1.88E-2 323.1 2.83E-2 3.03E-2 2.33E-2 1.88E-2 340 3.29E-1 3.47E-1 2.68E-1 2.21E-1 370 4.24E-1 4.47E-1 3.45E-1 2.86E-1 406 8.41E-1 8.83E-1 6.96E-1 5.97E-1 430 1.17 1.23 9.80E-1 8.40E-1 460 1.47 1.55 1.24 1.06 490 1.50 1.58 1.26 1.09 536.25 2.68 2.81 2.30 1.95 587.5 2.80 2.96 2.35 2.03 637.5 2.80 2.90 2.42 2.07 687.5 2.80 3.00 2.40 2.10 750 2.76 2.80 2.20 2.36 800 2.50 2.70 2.26 1.60
Module Section GQUAL
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Table 4.2(3).6-3 Solar Intensity Values for Latitude 30 N
Wavelength, Solar Intensity, milli-einsteins/cm2/day Nanometers Spring Summer Fall Winter 300 2.30E-4 3.65E-4 1.35E-4 4.10E-5 303.75 2.13E-3 2.32E-3 1.44E-3 6.50E-4 308.73 7.26E-3 9.02E-3 4.84E-3 2.76E-3 313.75 1.65E-2 1.92E-2 1.16E-2 7.55E-3 318.75 2.64E-2 3.02E-2 1.89E-2 1.31E-2 323.1 2.69E-2 3.04E-2 2.30E-2 1.34E-2 340 3.20E-1 3.74E-1 2.23E-1 1.70E-1 370 4.14E-1 4.37E-1 2.84E-1 2.19E-1 400 8.27E-1 9.07E-1 6.23E-1 4.75E-1 430 1.15 1.34 8.50E-1 6.69E-1 460 1.45 1.59 1.09 8.50E-1 490 1.48 1.62 1.11 8.80E-1 536.25 2.64 2.89 2.00 1.57 587.5 2.74 3.03 2.07 1.63 637.5 2.76 3.00 2.09 1.67 687.5 2.80 3.00 2.10 1.73 750 2.70 2.90 2.10 1.63 800 2.50 2.80 1.90 1.60
Table 4.2(3).6-4 Solar Intensity Values for Latitude 40 N
Wavelength, Solar Intensity, milli-einsteins/cm2/day Nanometers Spring Summer Fall Winter 300 1.09E-4 2.49E-4 1.09E-4 5.38E-6 303.75 1.37E-3 2.32E-3 1.37E-3 1.56E-4 308.75 2.96E-3 7.93E-3 5.35E-3 1.02E-3 313.75 7.99E-3 1.81E-2 1.38E-2 3.79E-3 318.75 1.38E-2 2.91E-2 2.319E-2 7.53E-3 323.1 1.42E-2 2.97E-2 2.39E-2 8.10E-3 340 1.78E-1 3.54E-1 1.08E-1 7.52E-2 370 2.30E-1 4.58E-1 3.84E-1 1.47E-1 400 5.26E-1 9.71E-1 7.91E-1 3.38E-1 430 6.76E-1 1.28 1.11 4.80E-1 460 8.90E-1 1.43 1.39 6.10E-1 490 9.23E-1 1.63 1.42 6.20E-1 536.25 1.69 2.92 2.52 1.12 587.5 1.73 3.05 2.62 1.16 637.5 1.78 3.00 2.60 1.19 687.5 1.50 3.10 4.70 1.39 750 1.70 2.90 2.60 1.20 800 1.60 2.90 2.50 1.16
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Table 4.2(3).6-5 Solar Intensity Values for Latitude 50 N
Wavelength, Solar Intensity, milli-einsteins/cm2/day Nanometers Spring Summer Fall Winter 300 3.71E-5 7.88E-6 1.52E-4 4.00E-7 303.75 7.10E-4 1.75E-3 2.25E-4 1.57E-5 308.75 3.55E-3 6.53E-3 1.29E-3 1.78E-4 313.75 7.30E-3 1.63E-2 4.39E-3 1.20E-3 318.75 1.84E-3 2.67E-2 8.64E-3 2.93E-3 323.1 1.96E-2 2.77E-2 9.20E-3 3.68E-3 340 2.66E-1 3.43E-1 1.24E-1 6.29E-2 370 3.48E-1 4.44E-1 1.66E-1 8.21E-2 400 7.24E-1 9.04E-1 3.65E-1 1.96E-1 430 1.02 1.26 5.17E-1 2.75E-1 460 1.29 1.60 6.60E-1 3.51E-1 470 1.32 1.63 6.80E-1 3.55E-1 536.25 2.34 2.90 1.22 6.30E-1 587.5 2.40 3.04 1.25 6.40E-1 637.5 2.44 3.00 1.31 6.90E-1 687.5 2.50 3.10 1.34 7.10E-1 750 2.50 2.90 1.31 7.10E-1 800 2.30 2.90 1.24 6.90E-1
Module Section GQUAL
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Volatilization
Volatilization of a chemical that is dissolved in water is defined as the transportof the chemical from the water to the atmosphere. The concentration of thechemical in water decreases even though a transformation does not occur. Thus,volatilization is not a degradation process in the strict sense, since the chemicalwhich leaves a water body by volatilization is not biologically or chemicallydegraded. Current evidence suggests that volatilization is likely to be the majoraquatic fate of low molecular weight, nonpolar compounds that are not rapidlybiodegraded or chemically transformed. Volatilization rates of higher molecularweight compounds can also be significant under certain conditions (Smith, 1979).
In HSPF, the volatilization rate of a qual is tied to the oxygen reaerationcoefficient:
KVOL = KOREA*CFGAS (18) where: KVOL = rate of loss of qual from water due to volatilization KOREA = oxygen reaeration coefficient calculated by subroutine OXREA CFGAS = ratio of volatilization rate of qual to oxygen reaeration rate, an input parameter.
The value for input parameter CFGAS can be determined as the ratio of the moleculardiameter of oxygen to the molecular diameter of the qual.
Biodegradation
Biodegradation is one of the most important processes for transformation ofchemical compounds when they enter natural environments. Many organic chemicalsare used by living cells for carbon and energy sources. Microorganisms metabolizea wide variety of organic compounds, including many man-made chemicals (Chou,1980). The rate of biodegradation of a dissolved qual is expressed as a functionof the concentration of biomass which degrades the qual (BIO) and watertemperature:
KBIO = KBMASS*BIO*(THBIO**TW20) (19)
where: KBIO = biodegradation rate constant for qual adjusted for biomass concentration and water temperature BIOCON = base biodegradation rate coefficient for qual BIO = concentration of biomass that is involved in qual degradation THBIO = temperature correction parameter for biodegradation TW20 = TW (water temperature in degrees C) - 20.0
Module Section GQUAL
199
Biomass data may be supplied as a constant, 12 monthly values, or a time series.HSPF allows for the fact that a different population of microorganisms can beinvolved in the biodegradation of each different generalized quality constituentby requiring the user to specify a unique set of biomass data for each constituentwhich is simulated.
Generalized First-order Decay
Generalized first-order decay of dissolved qual may be simulated in addition to,or instead of, the individual decay processes outlined above. The equation usedto calculate rate of decay is:
KGEN = KGEND*THGEN**TW20 (20) where: KGEN = generalized first-order decay rate for a qual corrected for temperature KGEND = base first-order decay rate for a qual THGEN = temperature correction parameter for first-order decay
After decay rates for all of the processes which are active for a qual have beencalculated, they are summed to determine a total decay rate. At this point thetotal loss of qual material resulting from decay is evaluated:
DDQALT = DQAL*(1.0 - EXP(-KTOTD))*VOL (21)
where: DDQALT = loss of qual due to all forms of degradation, expressed in (concu/l)*(ft3/ivl) or (concu/l)*(m3/ivl) DQAL = concentration of dissolved qual (concu/l) KTOTD = total decay rate of qual per interval VOL = volume of water in the RCHRES Finally, to determine the amount of material degraded by each individual process,a linear proration is performed based on the total decay of material:
DDQAL(I) = (K(I)/KTOTD)*DDQALT (22)
where: DDQAL(I) = loss of qual due to decay by process I, expressed in (concu/l)*(ft3/ivl) or (concu/l)*(m3/ivl) K(I) = decay rate due to process I (/ivl)
Module Section GQUAL
200
4.2(3).6.2 Simulate Advection of Material on Sediment (subroutine ADVQAL)
Purpose
ADVQAL simulates the advective processes for the quality constituent attached toone sediment size fraction. Processes handled in this subroutine include:
1. Inflow to the RCHRES of qual attached to suspended sediment.
2. Migration of qual from suspension in the water to the bed as a result ofdeposition of the sediment to which the qual is adsorbed.
3. Migration of qual from the bed into suspension in the water as a result of
scour of the bed sediments to which the qual is adsorbed.
4. Outflow from the RCHRES of qual attached to suspended sediment.
Method
The movement of adsorbed qual is completely determined by the movement of thesediment to which it is attached. All fluxes of adsorbed qual are expressed as theproduct of the flux of a sediment fraction (sand, silt, or clay) and theconcentration of qual associated with that fraction (expressed in concu per mg ofsediment). Likewise, storages of adsorbed qual are expressed as the product of thesediment fraction storage and the associated concentration of qual. A simplifiedflow diagram of sediment and associated qual fluxes and storages is provided inFigure 4.2(3).6-2 to facilitate the following discussion. Note that ADVQAL isdesigned to operate on one sediment fraction and one qual each time it is calledby GQUAL.
If the sediment simulation in module section SEDTRN indicates that scour of bedstorage of a sediment fraction occurs, the following actions are taken in ADVQAL:
1. Bed storage of adsorbed qual is updated. 2. Flux of qual from bed to suspension (DSQAL) is set equal to the bed storage
of the qual (RBQAL) if the entire bed storage of the sediment fraction isscoured.
3. If only part of the bed storage of the sediment fraction is scoured, the fluxof qual from bed to suspension is calculated as:
DSQAL = BQAL*DEPSCR (23)
Module Section GQUAL
201
Figure 4.2(3).6-2 Simplified flow diagram for important fluxes and storages of sediment and associated qual used in subroutine ADVQAL
Module Section GQUAL
202
where: DSQAL = amount of qual scoured from bed and added to suspension expressed in (concu/1)*(ft3/ivl) or (concu/1)*(m3/ivl) BQAL = concentration of qual on bed sediment fraction under consideration in concu/mg sediment DEPSCR = amount of sediment fraction which is scoured from the bed expressed in mg.ft3/1.ivl or mg.m3/l.ivl
4. Concentration of adsorbed qual in suspension is updated to account for scour: SQAL = (ISQAL + RSQALS - DSQAL)/(RSED + ROSED) (24)
where: SQAL = concentration of adsorbed qual in suspension expressed as concu/mg suspended sediment fraction ISQAL = inflow of qual to the RCHRES as a result of inflowing sediment fraction, expressed as (concu/l)*(ft3/ivl) or (concu/l)*(m3/ivl) RSQALS = storage of qual on suspended sediment fraction expressed in (concu/l)*ft3 or (concu/l)*m3 RSED = amount of sediment fraction in suspension at end of interval expressed in mg.ft3/l or mg.m3/l ROSED = amount of sediment fraction contained in outflow from the RCHRES during the interval expressed in mg.ft3/l.ivl or mg.m3/l.ivl
5. Amount of qual leaving the RCHRES as outflow is determined as:
ROSQAL = ROSED*SQAL (25)
If the sediment simulation in module section SEDTRN indicates that deposition ofsuspended sediment occurs, ADVQAL performs the following operations:
1. Concentration of qual on total suspended sediment fraction (inflow + suspendedstorage) for the RCHRES is calculated:
SQAL = (ISQAL + RSQALS)/(RSED + DEPSCR + ROSED) (26)
2. Amount of qual leaving the RCHRES due to outflow of sediment fraction isdetermined:
ROSQAL = ROSED*SQAL (27)
3. Amount of qual leaving suspension due to deposition of the sediment to whichit is adsorbed is found by:
DSQAL = DEPSCR*SQAL (28)
Module Section GQUAL
203
4. The concentration of qual on sediment in suspension is set equal to zero ifthe suspended storage of sediment is zero.
5. The concentration of qual on bed sediment is set equal to zero if the storageof bed sediment at the end of the interval is zero.
6. If there is bed sediment at the end of the interval, the bed storage of qualassociated with the sediment fraction is calculated as:
RBQAL = DSQAL + RBQALS (29)
7. The concentration of qual on bed sediment is determined:
BQAL = RBQAL/BSED (30)
where: BSED = storage of sediment fraction (sand, silt, or clay) in the bed, expressed as mg.ft3/l or mg.m3/l
The final operation which ADVQAL performs is the computation of outflow of adsorbedqual through individual exits (when more than one exit is specified). The algorithmis:
OSQAL (I) = ROSQAL*OSED(I)/ROSED (31)
where: OSQAL(I) = outflow of adsorbed qual through exit gate I ROSQAL = total outflow of adsorbed qual from RCHRES OSED(I) = outflow of sediment fraction through exit gate I
4.2(3).6.3 Simulate Decay of Adsorbed Material (subroutine ADECAY)
Purpose
ADECAY is a generalized subroutine which calculates the amount of decay experiencedby a generalized quality constituent (qual) adsorbed to inorganic sediment. Thissubroutine is called twice (once for decay on suspended sediment and once for decayon bed sediment) for each generalized quality constituent which is sediment-associated. (The user specifies that a qual is sediment-associated by settingQALFG(7)=1 for the qual in the User's Control Input.) HSPF assumes that the decayrate of a particular adsorbed qual is the same for all fractions of sediment (sand,silt, and clay), but may be different for suspended sediment than it is for bedsediment.
Module Section GQUAL
204
Method
Necessary information which must be supplied to the subroutine includes:
1. ADDCPM(1) - decay rate for qual on sediment being considered (suspended or bed)
2. ADDCPM(2) - temperature correction coefficient for decay
3. RSED(1-3) - the storage of each sediment fraction expressed in mg.ft3/l or mg.m3/l (for either suspended or bed sediment)
4. SQAL(1-3) - the concentration of qual associated with the 3 fractions of sediment (concu/mg)
First, the temperature-adjusted decay rate is calculated:
DK = ADDCPM(1)*ADDCPM(2)**TW20 (32)
where: TW20 = TW (water temperature) - 20.0 in degrees C.
Next, the fraction of adsorbed qual which decays during the simulation interval(FACT) is calculated using the general form for first-order decay:
FACT = 1.0 - EXP(-DK) (33)
The concentration of qual decayed from each sediment fraction (DCONC) isdetermined, and the concentration of qual associated with each fraction is updated:
DCONC = SQAL(I)*FACT (34)
SQAL(I) = SQAL(I) - DCONC (35)
Finally, the mass of qual decayed from each sediment fraction is calculated:
SQDEC(I) = DCONC*RSED(I) (36)
where: SQDEC(I) = amount of qual decayed from sediment fraction I expressed in (concu/l)*(ft3/ivl) or (concu/l)*(m3/ivl) DCONC = concentration of qual decayed from sediment fraction (concu/mg) RSED(I) = storage of sediment fraction I (mg.ft3/l or mg.m3/l)
Module Section GQUAL
205
4.2(3).6.4 Simulate Adsorption/Desorption of a Generalized Quality Constituent (subroutine ADSDES)
Purpose
ADSDES simulates the exchange of a generalized quality constituent (qual) betweenthe dissolved state and adsorbed state. Kinetic equilibrium between dissolvedstate and six adsorption sites is modeled: suspended sand, silt, and clay, and bedsand, silt, and clay.
Method
The basic equation (Onishi and Wise, 1979) for the transfer of a chemical betweenthe dissolved state and an adsorbed state on sediment type J is:
-d(RSEDJ*SQALJ)/dt + RSEDJ*KJT*(KDJ*DQAL - SQALJ) = 0 (37)
where: RSEDJ = total quantity of sediment type J in the RCHRES (mg.ft3/l or mg.m3/l) SQALJ = concentration of qual on sediment type J (concu/mg) DQAL = concentration of dissolved qual (concu/l) KDJ = distribution coefficient between dissolved state and sediment type J (liters/mg) (adsorbed concentration/dissolved concentration) KJT = temperature corrected transfer rate between dissolved state and sediment type J
Thus, adsorption of a qual by sediment or desorption from sediment is assumed tooccur toward an equilibrium condition with transfer rate KJT if the particulatequal concentration differs from its equilibrium value. Equation 37 is actually 6equations (one for each sediment type J) with 7 unknowns (DQAL and 6 values ofSQALJ). The necessary seventh equation is that of conservation of material. Thefollowing relation gives the total quantity of qual in the RCHRES, both before andafter exchange due to adsorption/desorption:
SUM [1 to 6](RSEDJ*SQALJ) + VOL*DQAL = TOT (38)
where: VOL = volume of water in the RCHRES
To solve numerically, Equation 37 is expressed in finite difference form:
-RSEDJ*(SQALJ - SQALJO) + RSEDJ*KJT*KDJ*DQAL*DELT (39) - RSEDJ*KJT*SQALJ*DELT = 0
where: SQALJ = concentration of qual on sediment type J at end of simulation interval (subsequent to adsorption/desorption) SQALJO = concentration of qual on sediment type J at start of interval DELT = simulation time step
Module Section GQUAL
206
The product of the transfer rate for sediment type J and the simulation time stepis calculated (AKJ = KJT*DELT), and the resulting value is substituted intoEquations 38 and 39. Two forms of Equation 38 are written. Equation 40 expressesconservation of material at the beginning of the simulation interval and Equation41 expresses conservation of material at the end of the interval:
- SUM [1 to 6] ((RSEDJ*SQALJO) - VOL*DQALO) = -TOT (40)
- SUM [1 to 6] ((RSEDJ*SQALJ) - VOL*DQAL ) = -TOT (41)
Equation 39 is rewritten as:
RSEDJ((1.0 + AKJ)/(AKJ*KDJ))*SQALJ - RSEDJ*DQAL = (42) (RSEDJ*SQALJO)/(AKJ*KDJ)
Equations 41 and 42 can be written in matrix form and solved for unknowns SQALJ andDQAL using standard procedures such as Gaussian elimination or the Crout reduction.The solutions are:
DQAL = (TOT - SUM [1 to 6] (RSEDJ*CJ)/AJJ)/ (43) (VOL + SUM [1 to 6] (RSEDJ/AJJ))
SQALJ = (CJ/AJJ) + (DQAL/AJJ) (44)
where: DQAL = concentration of dissolved qual after adsorption/desorption SQALJ = concentration of qual on sediment type J after adsorption/desorption AJJ = (1 + AKJ)/(AKJ*KDJ) CJ = (SQALJO/AKJ*KDJ)
By combining Equations 40 and 43, TOT can be eliminated, and a final solution forDQAL can be obtained:
DQAL = (VOL*DQALO + SUM [1 to 6] (SQALJO - CJ/AJJ)*RSEDJ)) (45) /(VOL + SUM [1 to 6] (RSEDJ/AJJ))
In subroutine ADSDES, the following variables are used to facilitate the evaluationof Equations 44 and 45:
AINVJ = 1.0/AJJ = (AKJ*KDJ)/(1.0 + AKJ) (46)
CAINVJ = CJ/AJJ = (SQALJO/(1.0 + AKJ)) (47)
Module Section RQUAL
207
4.2(3).7 Simulate Constituents Involved in Biochemical Transformations (Section RQUAL of Module RCHRES)
RQUAL is the parent routine to the four subroutine groups which simulateconstituents involved in biochemical transformations. Within module section RQUALthe following constituents may be simulated:
dissolved oxygen biochemical oxygen demand ammonia nitrite nitrate orthophosphorus phytoplankton benthic algae zooplankton dead refractory organic nitrogen dead refractory organic phosphorus dead refractory organic carbon total inorganic carbon pH carbon dioxide
Four additional quantities are estimated from simulation of these constituents.These quantities are total organic nitrogen, total organic phosphorus, totalorganic carbon, and potential biochemical oxygen demand. The definition of thesequantities is determined by their method of calculation:
TORN = ORN + CVBN*(ZOO + PHYTO + BOD/CVBO) (1) TORP = ORP + CVBP*(ZOO + PHYTO + BOD/CVBO) (2) TORC = ORC + CVBC*(ZOO + PHYTO + BOD/CVBO) (3) POTBOD = BOD + CVNRBO*(ZOO + PHYTO) (4)
where: TORN = total organic nitrogen (mg N/l) TORP = total organic phosphorus (mg P/l) TORC = total organic carbon (mg C/l) POTBOD = potential BOD (mg O/l) ORN = dead refractory organic nitrogen (mg N/l) ORP = dead refractory organic phosphorus (mg P/l) ORC = dead refractory organic carbon (mg C/l) BOD = biochemical oxygen demand from dead nonrefractory organic materials (mg O/l) CVBN = conversion from mg biomass to mg nitrogen CVBP = conversion from mg biomass to mg phosphorus CVBC = conversion from mg biomass to mg carbon CVNRBO = conversion from mg biomass to mg biochemical oxygen demand (with allowance for non-refractory fraction) CVBO = conversion from mg biomass to mg oxygen ZOO = zooplankton (mg biomass/l) PHYTO = phytoplankton (mg biomass/l)
Module Section RQUAL
208
Subroutine RQUAL performs two tasks. First, RQUAL is responsible for calling thefour subroutine groups which simulate the constituents listed above. These fourgroups and their functions are:
1. OXRX: simulate primary dissolved oxygen and biochemical oxygen demand balances 2. NUTRX: determine inorganic nitrogen and phosphorus balances 3. PLANK: simulate plankton populations and associated reactions 4. PHCARB: simulate pH and inorganic carbon species
The four groups are listed in their order of execution, and the execution of agroup is dependent upon the execution of the groups listed above it. For example,subroutine group PHCARB cannot be activated unless OXRX, NUTRX, and PLANK areactive. On the other hand, the reactions in OXRX can be performed without thereactions contained in the other three subroutine groups.
The other function of RQUAL is to determine the values for variables which are usedjointly by the four subroutine groups. The following variables are evaluated:
1. AVVELE: the average velocity of water in the RCHRES (ft/s) 2. AVDEPE: the average depth of water in the RCHRES (ft) 3. DEPCOR: conversion factor from square meters to liters (used for changing areal quantities from the benthal surface to equivalent volumetric values based on the depth of water in the RCHRES) 4. SCRFAC: scouring factor to be used for calculation of benthal release rates of inorganic nitrogen, orthophosphorus, carbon dioxide, and biochemical oxygen demand
SCRFAC has one of two values depending on the average velocity (AVVELE) of thewater in the RCHRES. AVVELE is compared to the value of parameter SCRVEL, the user-specified velocity at and above which scouring occurs. If AVVELE is less than thevalue of parameter SCRVEL, then SCRFAC is set equal to 1.0, and there is noincrease of benthal release rates due to scouring. If AVVELE is greater thanSCRVEL, SCRFAC is set equal to the value of parameter SCRMUL, which is a constantmultiplication factor applied directly to the release rates to account for scouringby rapidly moving water.
Subroutine Group OXRX
209
4.2(3).7.1 Simulate Primary DO and BOD Balances (Subroutine Group OXRX of Module RCHRES)
Purpose
The purpose of this code is to simulate the primary processes which determine thedissolved oxygen concentration in a reach or mixed reservoir. Dissolved oxygenconcentration is generally viewed as an indicator of the overall well-being ofstreams or lakes and their associated ecological systems. In relatively unpollutedwaters, sources and sinks of oxygen are in approximate balance, and theconcentration remains close to saturation. By contrast, in a stream receivinguntreated waste waters, the natural balance is upset, bacteria predominate, and asignificant depression of dissolved oxygen results (O'Connor and DiToro, 1970). Schematic View of Fluxes and Storages Figures 4.2(3).7.1-1 and 4.2(3).7.1-2 illustrate the fluxes and storages modeledin this subroutine group. In order to account for temporal variations in oxygenbalance, state variables for both dissolved oxygen and biochemical oxygen demandmust be maintained. The state variable DOX represents the oxygen dissolved inwater and immediately available to satisfy the oxygen requirements of the system.The BOD state variable represents the total quantity of oxygen required to satisfythe first-stage (carbonaceous) biochemical oxygen demand of dead nonrefractoryorganic materials in the water. Subroutine OXRX considers the following processes in determining oxygen balance: 1. longitudinal advection of DOX and BOD 2. sinking of BOD material 3. benthal oxygen demand 4. benthal release of BOD material 5. reaeration 6. oxygen depletion due to decay of BOD materials
Additional sources and sinks of DOX and BOD are simulated in other sections of theRCHRES module. If module section NUTRX (Section 4.2(3).7.2) is active, the effectsof nitrification on dissolved oxygen and denitrification on BOD balance can beconsidered. If module section PLANK (Section 4.2(3).7.3) is active, the dissolvedoxygen balance can be adjusted to account for photosynthetic and respiratoryactivity by phytoplankton and/or benthic algae and respiration by zooplankton.Adjustments to the BOD state variable in section PLANK include increments due todeath of plankton and nonrefractory organic excretion by zooplankton.
Subroutine Group OXRX
210
Figure 4.2(3).7.1-1 Flow diagram for dissolved oxygen in the OXRX subroutine group of the RCHRES Application Module
Figure 4.2(3).7.1-2 Flow diagram for biochemical oxygen demand in the OXRX subroutine group of the RCHRES Application Module
Subroutine Group OXRX
211
Subroutine OXRX uses five subroutines to simulate dissolved oxygen and biochemicaloxygen demand. Advection of DOX and BOD is performed by ADVECT. Sinking of BODmaterial is carried out by SINK. OXBEN calculates benthal oxygen demand andbenthal release of BOD materials. The oxygen reaeration coefficient is determinedby utilizing OXREA, and BOD decay calculations are performed in BODDEC.
Since subroutine OXREA may also be called by module section GQUAL to obtain theoxygen reaeration coefficient (KOREA) for calculation of volatilization rates forgeneralized quality constituents, the change in dissolved oxygen concentration inwater due to reaeration is calculated in OXRX rather than OXREA. The equation forreaeration is: DOX = DOXS + KOREA*(SATDO - DOXS) (1) where: DOX = dissolved oxygen concentration after reaeration (mg/l) DOXS = dissolved oxygen concentration at start of interval (mg/l) KOREA = reaeration coefficient calculated in OXREA SATDO = saturated concentration of dissolved oxygen (mg/l) The saturation concentration of dissolved oxygen is computed at prevalentatmospheric conditions by the equation: SATDO = (14.652 + TW*(-0.41022 + TW*(0.007991 - 0.7777E-4*TW)))* (2) CFPRES where: SATDO = saturated concentration of dissolved oxygen (mg/l) TW = water temperature (deg C) CFPRES = ratio of site pressure to sea level pressure (CFPRES is calculated by the Run Interpreter dependent upon mean elevation of RCHRES)
Subroutine Group OXRX
212
4.2(3).7.1.1 Simulate Benthal Oxygen Demand and Benthal Release of BOD (subroutine OXBEN)
Purpose OXBEN accounts for two possible demands exerted on available oxygen by the benthos.These two demands are categorized as benthal oxygen demand and benthal release ofBOD materials. Benthal oxygen demand results from materials in the bottom mudswhich require oxygen for stabilization. This process results in a direct loss ofoxygen from the RCHRES. The second demand on oxygen caused by the release andsuspension of BOD materials is a less direct form of oxygen demand. This processincreases the pool of BOD present in the RCHRES and exerts a demand on thedissolved oxygen concentration at a rate determined by the BOD decompositionkinetics.
Benthal Oxygen Demand The user approximates the oxygen demand of the bottom muds at 20 degrees Celsiusby assigning a value to BENOD for each RCHRES. The effects of temperature anddissolved oxygen concentration on realized benthal demand are determined by thefollowing equation: BENOX = BENOD*(TCBEN**TW20)*(1.0 - Exp(-EXPOD*DOX)) (3) where: BENOX = amount of oxygen demand exerted by benthal muds (mg/m2/interval) BENOD = reach dependent benthal oxygen demand at 20 degrees C (mg/m2/interval) TCBEN = temperature correction factor for benthal oxygen demand TW20 = water temperature - 20.0 (deg C) EXPOD = exponential factor to benthal oxygen demand function (default value = 1.22) DOX = dissolved oxygen concentration (mg/l) The first portion of the above equation adjusts the demand at 20 degrees Celsiusto a demand at any temperature. The second portion of the equation indicates thatlow concentrations of dissolved oxygen suppress realized oxygen demand. Forexample, 91 percent of BENOD may be realized at a dissolved oxygen concentrationof 2 mg/l, 70 percent at 1 mg/l, and none if the waters are anoxic. After the value of BENOX has been calculated, the dissolved oxygen state variableis updated: DOX = DOX - BENOX*DEPCOR (4) where: DEPCOR = factor which converts from mg/m2 to mg/l, based on the average depth of water in the RCHRES during the simulation interval (DEPCOR is calculated in subroutine RQUAL
Subroutine Group OXRX
213
Benthal Release of BOD Bottom releases of BOD are a function of scouring potential and dissolved oxygenconcentration. The equation used to calculate BOD release is: RELBOD = (BRBOD(1) + BRBOD(2)*Exp(-EXPREL*DOX))*SCRFAC (5) where: RELBOD = BOD released by bottom muds (mg/m2 per interval) BRBOD(1) = base release rate of BOD materials (aerobic conditions) (mg/m2/interval) BRBOD(2) = increment to bottom release rate due to decreasing dissolved oxygen concentration EXPREL = exponential factor to BOD benthal release function (default value = 2.82) DOX = dissolved oxygen concentration (mg/l) SCRFAC = scouring factor dependent on average velocity of water (SCRFAC is calculated in subroutine RQUAL)
The above equation accounts for the fact that benthal releases are minimal duringconditions of low velocity and ample dissolved oxygen. Under these conditions athin layer of hardened, oxidized material typically retards further release ofmaterials from the benthos. However, anaerobic conditions or increased velocityof overlying water disrupts this layer, and release rates of BOD and othermaterials are increased. Solution of Equation 3 indicates that 6 percent of theincremental release rate (BRBOD(2)) occurs when 1 mg/l of dissolved oxygen ispresent, 75 percent occurs when 0.1 mg/l is present, and the entire incrementoccurs under anoxic conditions.
4.2(3).7.1.2 Calculate Oxygen Reaeration Coefficient (subroutine OXREA)
Purpose Various methods have been used to calculate atmospheric reaeration coefficients,and experience has shown that the most effective method of calculation in any givensituation depends upon the prevalent hydraulic characteristics of the system(Covar, 1976). Based upon user instructions, subroutine OXREA calculates oxygenreaeration by using one of four built-in solution techniques.
Approach The general equation for reaeration is: DOX = DOXS + KOREA*(SATDO - DOXS) (6) where: DOX = dissolved oxygen concentration after reaeration (mg/l) KOREA = reaeration coefficient (greater than zero and less than one) SATDO = oxygen saturation level for given water temperature (mg/l) DOXS = dissolved oxygen concentration at start of interval (mg/l)
Subroutine Group OXRX
214
Lake Reaeration In a lake or reservoir, calculation of reaeration is dependent upon surface area,volume, and wind speed. The wind speed factor is determined using the followingempirical relationship: WINDF = WINDSP*(-0.46 + 0.136*WINDSP) (7) where: WINDF = wind speed factor in lake reaeration calculation WINDSP = wind speed (m/sec)
For low wind speeds, less than 6.0 m/s, WINDF is set to 2.0. The reaerationcoefficient for lakes is calculated as: KOREA = (.032808*WINDF*CFOREA/AVDEPE)*DELT60 (8) where: CFOREA = correction factor to reaeration coefficient for lakes; for lakes with poor circulation characteristics, CFOREA may be less than 1.0, and lakes with exceptional circulation characteristics may justify a value greater than 1.0 AVDEPE = average depth of water in RCHRES during interval (ft) DELT60 = conversion from hourly time interval to simulation interval
Stream Reaeration One of three approaches to calculating stream reaeration may be used: 1. Energy dissipation method (Tsivoglou-Wallace, 1972). Oxygen reaeration is
calculated based upon energy dissipation principles: KOREA = REAKT*(DELTHE/FLOTIM)*(TCGINV**(TW - 20.))*DELTS (9) where: REAKT = escape coefficient with a typical value between 0.054/ft and 0.110/ft. DELTHE = drop in energy line along length of RCHRES (ft) FLOTIM = time of flow through RCHRES (seconds) TCGINV = temperature correction coefficient for gas invasion rate with a default value of 1.047 DELTS = conversion factor from units of /second to units of /interval
DELTHE, the drop in elevation over the length of the RCHRES, is supplied by theuser. REAKT, the escape coefficient, referred to in Tsivoglou's work, is alsosupplied by the user. The value for FLOTIM is calculated by dividing the lengthof the RCHRES by the average velocity for the simulation interval. Tsivoglou'smethod of calculation is activated by setting the reaeration method flag (REAMFG)to 1.
Subroutine Group OXRX
215
2. Covar's method of determining reaeration (Covar, 1976). Reaeration iscalculated as a power function of hydraulic depth and velocity. The generalequation is:
KOREA = REAK*(AVVELE**EXPREV)*(AVDEPE**EXPRED) (10) *(TCGINV**(TW - 20.))*DELT60
where: KOREA = reaeration coefficient (per interval) REAK = empirical constant for reaeration equation (/hour) AVVELE = average velocity of water (ft/s) EXPREV = exponent to velocity function AVDEPE = average water depth (ft) EXPRED = exponent to depth function TCGINV = temperature correction coefficient for reaeration defaulted to 1.047 DELT60 = conversion factor from units of per hour to units of per interval
Depending on current depth and velocity, one of three sets of values for REAK,EXPREV, and EXPRED is used. Each set corresponds to an empirical formulawhich has proven accurate for a particular set of hydraulic conditions. Thethree formulas and their associated hydraulic conditions and coefficients are:
1. Owen's formula (Owen et al., 1964). This formula is used for depths ofless than 2 ft. For this formula, REAK = 0.906, EXPREV = 0.67, andEXPRED = -1.85.
2. Churchill's formula (1962). This formula is used for high velocitysituations in depths of greater than 2 ft. For this formula, REAK =0.484, EXPREV = 0.969, and EXPRED = -1.673.
3. O'Connor-Dobbins formula (1958). This formula is used for lower velocitysituations in depths of greater than 2 ft. The coefficient values are:REAK = 0.538, EXPREV = 0.5, and EXPRED = -1.5.
This method of calculation of reaeration is activated by setting thereaeration method flag (REAMFG) to 2.
3. Users may select their own power function of hydraulic depth and velocity for
use under all conditions of depth and velocity. In this case, the usersupplies values for REAK, EXPREV, and EXPRED. This option is selected bysetting the reaeration method flag (REAMFG) to 3.
Reaeration may be modeled as a constant process for any given temperature. In thiscase, the user must supply a value for REAK, and a value of zero for both EXPREVand EXPRED. Note that subroutine OXREA requires input values for REAK, EXPREV, andEXPRED only if REAMFG is 3.
Subroutine Group OXRX
216
4.2(3).7.1.3 Calculate BOD Decay (subroutine BODDEC)
Purpose Subroutine BODDEC adjusts the dissolved oxygen concentration of the water toaccount for the oxygen consumed by microorganisms as they break down complexmaterials to simpler and more stable products. Only carbonaceous BOD is consideredin this subroutine. The BOD decay process is assumed to follow first-orderkinetics and is represented by: BODOX = (KBOD20*(TCBOD**(TW - 20.)))*BOD (11) where: BODOX = quantity of oxygen required to satisfy BOD decay (mg/l per interval) KBOD20 = BOD decay rate at 20 degrees C (/interval) TCBOD = temperature correction coefficient, defaulted to 1.075 TW = water temperature (degrees C) BOD = BOD concentration (mg/l) If there is not sufficient dissolved oxygen available to satisfy the entire demandexerted by BOD decay, only the fraction which can be satisfied is subtracted fromthe BOD state variable, and the DOX variable is set to zero.
Subroutine Group NUTRX
217
4.2(3).7.2 Simulate Primary Inorganic Nitrogen and Phosphorus Balances (Subroutine Group NUTRX of Module RCHRES)
Purpose
This code simulates the primary processes which determine the balance of inorganicnitrogen and phosphorus in natural waters. When modeling the water quality of anaquatic system, consideration of both nitrogen and phosphorus is essential.Nitrogen, in its various forms, can deplete dissolved oxygen levels in receivingwaters, stimulate aquatic growth, exhibit toxicity toward aquatic life, or presenta public health hazard (EPA, 1975). Phosphorus is vital in the operation of energytransfer systems in biota, and in many cases is the growth limiting factor foralgal communities. Consequently, it is necessary to model phosphorus in any studyconcerned with eutrophication processes.
Schematic View of Fluxes and Storages
Figures 4.2(3).7.2-1 and 4.2(3).7.2-2 illustrate the fluxes and storages of fourconstituents which are introduced into the RCHRES modeling system in subroutinegroup NUTRX. In addition to these constituents, the state variables for dissolvedoxygen and BOD are also updated. If subroutine group NUTRX is active (NUTFG = 1),nitrate will automatically be simulated; the user must specify whether or notnitrite, total ammonia, and/or orthophosphorus are to be simulated in addition tonitrate by assigning appropriate values to NO2FG, TAMFG, and PO4FG in the User'sControl Input. In addition, if ammonia or orthophosphorus is simulated, the usermay specify whether to simulate the adsorbed (particulate) forms of ammonia andorthophosphorus. If either adsorbed nutrient is simulated, Section SEDTRN must beactive to provide the inorganic sediment (sand, silt, and clay) concentrations andfluxes. If all possible constituents are simulated, subroutine NUTRX considers thefollowing processes: 1. longitudinal advection of dissolved NO3, NO2, NH3, and PO4 2. benthal release of inorganic nitrogen (NH3) and PO4 (if BENRFG = 1) 3. ammonia ionization (NH3/NH4 + equilibrium) 4. ammonia vaporization (if AMVFG = 1) 5. nitrification of NH3 and NO2 6. denitrification of NO3 (if DENFG = 1) 7. ammonification due to degradation of BOD materials 8. adsorption/desorption of NH3 and PO4 to inorganic sediment in the water column (if ADNHFG = 1 or ADPOFG = 1) 9. deposition/scour and longitudinal advection of adsorbed NH3 and PO4 (if ADNHFG = 1 or ADPOFG = 1)
Additional sources and sinks of NO3, NH3, and PO4 are simulated in the PLANKsection (4.2(3).7.3) of this module. If section PLANK is active, the statevariables for these three constituents can be adjusted to account for nutrientuptake by phytoplankton and/or benthic algae, and for respiration and inorganicexcretion by zooplankton.
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Figure 4.2(3).7.2-1 Flow diagram for inorganic nitrogen in the NUTRX subroutine group of the RCHRES Application Module
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Figure 4.2(3).7.2-2 Flow diagram for ortho-phosphate in the NUTRX subroutine group of the RCHRES Application Module
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Subroutine NUTRX utilizes nine principal routines to simulate inorganic nitrogenand phosphorus. Advection of dissolved NO3, NO2, NH3, and PO4 is performed byADVECT. BENTH determines the amount of inorganic nitrogen and phosphorus which isreleased to the overlying waters from the benthos. The nitrification anddenitrification processes are simulated by NITRIF and DENIT, respectively.Adsorption/desorption of NH3 and PO4 is computed by ADDSNU, and the advection anddeposition/scour of the adsorbed forms are simulated in ADVNUT. The ammoniaionization and volatilization calculations are performed in AMMION and NH3VOL,respectively. Finally, the production of inorganic nitrogen and phosphorusresulting from decay of BOD materials is simulated by DECBAL.
Before ADVECT is called, NUTRX sums the inputs of dissolved NO3, NH3, and PO4 fromupstream reaches, tributary land areas, and atmospheric deposition (deposition ofNO2 is not considered):
INNUT = INUT + SAREA*ADFX + SAREA*PREC*ADCN (1)
where: INNUT = total input of dissolved nutrient to reach INUT = input of dissolved nutrient from upstream reaches and tributary land SAREA = surface area of reach ADFX = dry or total atmospheric deposition flux in mass/area per interval PREC = precipitation depth ADCN = concentration for wet atmospheric deposition in mass/volume
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series tocompute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series are documented in the EXTNL table of the Time Series Catalog forRCHRES, and are specified in the EXT SOURCES block of the UCI. Monthly values areinput in the MONTH-DATA block in the UCI.
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4.2(3).7.2.1 Simulate Benthal Release of Constituents (subroutine BENTH)
Purpose This subroutine checks to see whether present water conditions are aerobic oranaerobic, calculates benthal release for a constituent based on this check, andupdates the concentration of the constituent.
Approach
The equation used to calculate release is:
RELEAS = BRCON(I)*SCRFAC*DEPCOR (1)
where: RELEAS = amount of constituent released (mg/l per interval) BRCON(I) = benthal release rate (BRTAM or BRPO4) for constituent (mg/m2 per interval) SCRFAC = scouring factor, dependent on average velocity of the water (SCRFAC is computed in RQUAL) DEPCOR = conversion factor from mg/m2 to mg/l (computed in RQUAL)
The dissolved oxygen concentration below which anaerobic conditions are consideredto exist is determined by the input parameter ANAER. Two release rates arerequired for each of the constituents: one for aerobic conditions and one foranaerobic conditions. Typically, the aerobic release rate is less than theanaerobic rate, because a layer of oxidized materials forms on the benthal surfaceduring aerobic periods, and this layer retards the release rate of additionalbenthal materials. BRCON(1) is the aerobic release rate and BRCON(2) is theanaerobic rate. The choice of which release rate is used is determined bycomparing the current value of DOX to ANAER.
If ammonia is simulated, the inorganic nitrogen release from the benthos is assumedto be in the form of ammonia, and the NH3 (TAM) state variable is updated. Ifammonia is not simulated, benthal release of inorganic nitrogen is assumed to notoccur. If orthophosphate is simulated, an additional call is made to BENTH toaccount for release of PO4. Simulation of benthal release processes is activated by assigning a value of oneto BENRFG in the User's Control Input for RQUAL.
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4.2(3).7.2.2 Simulate Nitrification (subroutine NITRIF)
Purpose NITRIF simulates the oxidation of ammonium and nitrite by chemoautotrophicbacteria. This oxidation provides energy for bacteria much the same way thatsunlight provides energy for photosynthetic algae. The Nitrosomonas genera areresponsible for conversion of ammonium to nitrite, and Nitrobacter performoxidation of nitrite to nitrate. Oxidation of inorganic nitrogen is dependent upona suitable supply of dissolved oxygen; subroutine NITRIF does not simulatenitrification if the DO concentration is below 2 mg/l.
Method
The rate of nitrification is represented by a first order equation in whichnitrification is directly proportional to the quantity of reactant present, eitherammonia or nitrite. The equation used to calculate the amount of NH3 oxidized toNO2 is: TAMNIT = KTAM20*(TCNIT**(TW - 20.))*TAM (2) where: TAMNIT = amount of NH3 oxidation (mg N/l per interval) KTAM20 = ammonia oxidation rate coefficient at 20 degrees C (/interval) TCNIT = temperature correction coefficient, defaulted to 1.07 TW = water temperature (degrees C) TAM = total ammonia concentration (mg N/l)
Similarly, if nitrite is simulated, the amount of nitrite oxidized to nitrate isdetermined by the equation: NO2NIT = KNO220 * (TCNIT**(TW - 20.)) * NO2 (3) where: NO2NIT = amount of NO2 oxidation (mg N/l/interval KNO220 = NO2 oxidation rate coefficient at 20 degrees C (/interval) NO2 = nitrite concentration (mg N/l) The amount of oxygen used during nitrification is 3.43 mg oxygen per mg NH3-Noxidized to NO2-N, and 1.14 mg oxygen per mg NO2-N oxidized to NO3-N. In theRCHRES module, these figures are adjusted to 3.22 mg and 1.11 mg, respectively, toaccount for the effects of carbon dioxide fixation by bacteria (Wezerak and Gannon,1968). Thus, the oxygen demand due to nitrification is evaluated as: DODEMD = 3.22 * TAMNIT + 1.11 * NO2NIT (4) where: DODEMD = loss of dissolved oxygen from the RCHRES due to nitrification (mg O/l per interval)
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If the value of DODEMD is greater than available dissolved oxygen, the amounts ofoxidation from NH3 to NO2 and from NO2 to NO3 are proportionally reduced, so thatthe state variable DOX maintains a non-negative value. If nitrite is notsimulated, the calculated amount of oxidized ammonia is assumed to be fullyoxidized to nitrate.
4.2(3).7.2.3 Simulate Denitrification (subroutine DENIT)
Purpose DENIT simulates the reduction of nitrate by facultative anaerobic bacteria such asPseudomonas, Micrococcus, and Bacillus. These bacteria can use NO3 for respirationin the same manner that oxygen is used under aerobic conditions. Facultativeorganisms use oxygen until the environment becomes nearly or totally anaerobic, andthen switch over to NO3 as their oxygen source. In HSPF, the end product ofdenitrification is assumed to be nitrogen gas.
Approach
Denitrification does not occur in the RCHRES module unless the dissolved oxygenconcentration is below a user-specified threshold value (DENOXT). If thatsituation occurs, denitrification is assumed to be a first-order process based onthe NO3 concentration. The amount of denitrification for the interval iscalculated by the following equation:
DENNO3 = KNO320 * (TCDEN**(TW-20)) * NO3 (5) where: DENNO3 = amount of NO3 denitrified (mg N/l per interval) KNO320 = NO3 denitrification rate coefficient at 20 degrees C (/interval) TCDEN = temperature correction coefficient for denitrification NO3 = nitrate concentration (mg N/l)
4.2(3).7.2.4 Simulate Adsorption/Desorption of Ammonia and Orthophosphorus (subroutine ADDSNU)
Purpose
This subroutine simulates the exchange of nutrient (ammonium and orthophosphorus)between the dissolved state and adsorption on suspended sediment. The sorbentsconsidered are suspended sand, silt, and clay, which are simulated in sectionSEDTRN. The adsorption/desorption process is not simulated in bed sediments.
Approach
The adsorption/desorption for each sediment fraction is represented with anequilibrium, linear isotherm, i.e., a standard Kd approach, which is described asfollows:
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SNUT(J) = DNUT * ADPM(J) (6)
where: SNUT(J) = equilibrium concentration of adsorbed nutrient on sediment fraction J (mg/kg) DNUT = the equilibrium concentration of dissolved nutrient (mg/l) ADPM(J) = adsorption parameter (or Kd) for sediment fraction J (l/kg)
This expression for SNUT(J) is substituted into the following mass balanceexpression for total nutrient in the reach:
NUM = DNUT*VOL + 3 [SNUT(J)*RSED(J)] = total nutrient in reach (7) J=1,3
where: NUM = variable used to represent total nutrient mass in the reach (mg) VOL = volume of reach (l) RSED(J) = mass of sediment fraction J in suspension (kg)
After substituting, rearranging, and solving for DNUT, the following expression isobtained:
NUM DNUT = S)))))))))))))))))))))))Q (8) VOL + 3 [RSED(J)*ADPM(J)] J=1,3
In the above equation, the value of NUM is obtained from a "non-equilibrium"version of Equation (7) in which temporary DNUT and SNUT values include the effectsof other processes such as advection, scour/deposition, nitrification, etc, thathave occurred during the interval. Therefore, the overall procedure involvesperforming all processes that affect the nutrient concentrations, and thenpartitioning (equilibrating) the total mass of nutrient among the four phases,i.e., dissolved phase and three sediment fractions.
Note, the units listed for some variables in the preceding discussion aresimplified from the internal (code) HSPF units.
4.2(3).7.2.5 Simulate Advection and Deposition/Scour of Adsorbed Ammonia and Orthophosphorus (subroutine ADVNUT)
Purpose ADVNUT simulates the advective processes for a nutrient (NH3 or PO4) attached toone sediment size fraction. Processes handled in this routine include:
1. Inflow to the RCHRES of nutrient attached to suspended sediment.
2. Migration of nutrient from suspension in the water to the bed as a result ofdeposition of the sediment to which the nutrient is adsorbed.
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3. Migration of nutrient from the bed into suspension in the water as a resultof scour of the bed sediments to which the nutrient is adsorbed.
4. Outflow from the RCHRES of nutrient attached to suspended sediment.
Method
The movement of adsorbed nutrient is completely determined by the movement of thesediment to which it is attached. All fluxes of adsorbed nutrient are expressedas the product of the flux of a sediment fraction (sand, silt, or clay) and theconcentration of nutrient associated with that fraction (expressed in mg per kg ofsediment). Likewise, storages of adsorbed nutrient are expressed as the productof the sediment fraction storage and the associated concentration of nutrient.Note that the nutrient storage in the bed is essentially infinite. Nutrients thatdeposit to the bed are assumed to be lost from the RCHRES, and scoured sediment isassumed to have a constant (user-specified) adsorbed nutrient concentration; thusthe scoured nutrient flux is limited only by the storage of sediment in the bed.A simplified flow diagram of sediment and associated nutrient fluxes and storagesis provided in Figure 4.2(3).7.2-3 to facilitate the following discussion. ADVNUTis designed to operate on one sediment fraction and one nutrient each time it iscalled by subroutine NUTRX.
If the sediment simulation in module section SEDTRN indicates that scour of bedstorage of a sediment fraction occurs, the following actions are taken in ADVNUT: 1. The flux of nutrient from bed to suspension is calculated as: DSNUT = BNUT*DEPSCR (9)
where: DSNUT = amount of nutrient scoured from bed and added to suspension (mg/l)*(ft3/ivl) or (mg/l)*(m3/ivl) BNUT = constant concentration of nutrient on bed sediment fraction under consideration (mg/mg sediment) DEPSCR = amount of sediment fraction which is scoured from the bed (mg.ft3/l.ivl or mg.m3/l.ivl) 2. The concentration of adsorbed nutrient in suspension is updated to account for
scour: SNUT = (ISNUT + RSNUTS - DSNUT)/(RSED + ROSED) (10)
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where: SNUT = concentration of adsorbed nutrient in suspension (mg/mg suspended sediment) ISNUT = inflow of nutrient to the RCHRES as a result of inflowing sediment fraction ((mg/l)*(ft3/ivl) or (mg/l)*(m3/ivl)) RSNUTS = storage of nutrient on suspended sediment fraction ((mg/l)*ft3 or (mg/l)*m3) RSED = amount of sediment fraction in suspension at end of interval (mg.ft3/l or mg.m3/l) ROSED = amount of sediment fraction contained in outflow from the RCHRES during the interval (mg.ft3/l.ivl or mg.m3/l.ivl) 3. The concentration of nutrient on bed sediment is set equal to zero if the
storage of bed sediment at the end of the interval is zero.
5. Amount of nutrient leaving the RCHRES as outflow is determined as: ROSNUT = ROSED*SNUT (11) If the sediment simulation in module section SEDTRN indicates that deposition ofsuspended sediment occurs, ADVNUT performs the following operations: 1. Concentration of nutrient on total suspended sediment fraction (inflow +
suspended storage) for the RCHRES is calculated: SNUT = (ISNUT + RSNUTS)/(RSED + DEPSCR + ROSED) (12) 2. Amount of nutrient leaving the RCHRES due to outflow of sediment fraction is
determined: ROSNUT = ROSED*SNUT (13) 3. Amount of nutrient leaving suspension due to deposition of the sediment to
which it is adsorbed is found by: DSNUT = DEPSCR*SNUT (14)
4. The concentration of nutrient on sediment in suspension is set equal to zeroif the suspended storage of sediment is zero.
The final operation which ADVNUT performs is the computation of outflow of adsorbednutrient through individual exits (when more than one exit is specified). Thealgorithm is: OSNUT(I) = ROSNUT*OSED(I)/ROSED (15) where: OSNUT(I) = outflow of adsorbed nutrient through exit gate I ROSNUT = total outflow of adsorbed nutrient from RCHRES OSED(I) = outflow of sediment fraction through exit gate I
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4.2(3).7.2.6 Simulate Ionization of Ammonia to Ammonium (subroutine AMMION)
Approach
The total dissolved ammonia state variable (TAM) consists of two forms, NH 4+ and
NH3. The ionized form is dominant at typical pH's and temperatures found in nature;however, the un-ionized form is toxic to aquatic species at fairly lowconcentrations, and may be significant at some extreme environmental pH's.Therefore, while the process formulations in HSPF are based on the total ammonia,the un-ionized form is computed and output.
The fraction (FRAC) of total ammonia that is present as un-ionized ammonia iscalculated as: 10pH
FRAC = )))))))))))) (16) 10pH + RATIO
where:
RATIO = ratio of ionization products for water k w and ammonia (kb)
RATIO is computed using an empirical relationship based on pH and temperature asdescribed by Loehr et al. (1973):
RATIO = -3.39753 loge(0.02409 TW) 109 (17)
The pH used in Equation 16 may be obtained from Section PHCARB (if it is active)or specified by the user in the form of a constant value, 12 monthly values, or aninput time series.
4.2(3).7.2.7 Simulate Ammonia Volatilization (subroutine NH3VOL)
Approach
The amount of total ammonia lost from the RCHRES due to ammonia volatilization iscalculated by a standard two-layer model of mass transfer across the air-waterinterface; this is based on Henry's Law and the flux of mass through the water andair films. The inverse of the overall mass transfer coefficient is given by thefollowing expression: 1 1 8.21x10 -5 * TWKELV S)Q = KRINV = S)))Q + S))))))))))))))))Q (18) KR NH3KL HCNH3 * NH3KG
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where: KR = overall mass transfer coefficient (cm/hr) KRINV = inverse of coefficient (hr/cm) NH3KL = liquid film mass transfer coefficient (cm/hr) NH3KG = gas film mass transfer coefficient (cm/hr) HCNH3 = Henry's Law Constant for ammonia (atm-m 3/mole) 8.21E-5 = the ideal gas constant (atm-m 3/K/mole) TWKELV = water temperature (degrees K)
Computation of the liquid-film coefficient is based on correlation with thereaeration rate (i.e., the rate of transfer of oxygen gas across the interface).The proportionality constant is a function of the ratio of the molecular weights.Therefore, the liquid-film coefficient is given by:
NH3KL = [KOREA * AVDEPM * 100/DELT60] * [1.878**(EXPNVL/2.)] (19)
where: KOREA = the oxygen reaeration rate (per interval) AVDEPM = average depth of the reach (m) 100 = conversion from meters to centimeters DELT60 = conversion from units of per interval to units of per hour 1.878 = ratio of molecular weight of oxygen (32) to ammonia (17) EXPNVL = user-specified exponential factor
Note that in the first part of the above equation, KOREA is being converted to thesame units as NH3KL, i.e., cm/hr.
In a similar manner to the liquid-film coefficient, the gas-film coefficient iscomputed from the water evaporation rate which is primarily driven by the wind.The gas film coefficient is computed as:
NH3KG = 700. * WINDSP * 1.057**(EXPNVG/2.) (20)
where: 700 = an empirical constant relating the wind speed in m/s and the evaporation rate in cm/hr WINDSP = wind speed (m/s) 1.057 = ratio of water molecular weight to that of ammonia EXPNVG = user-specified exponential factor
The Henry's constant for ammonia (HCNH3) is interpolated from a table of valuesbased on temperature and pH.
The reach-specific, first-order rate constant for volatilization is computed by:
KNVOL = KR * DELT60/(AVDEPM * 100) (21)
where: KNVOL = first-order rate constant for volatilization (/interval) 100 = conversion from units of 1/cm to 1/m
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Finally, the volatilization loss is computed as:
NH3VLT = KNVOL * TAM (22)
where: NH3VLT = volatilization loss during the interval (mg N/l) TAM = concentration of total ammonia (mg N/l)
Simulation of ammonia volatilization is activated by setting AMVFG equal to one inthe User's Control Input. Of course, total ammonia simulation must also beactivated by setting TAMFG equal to 1.
4.2(3).7.2.8 Perform Materials Balance for Transformation from Organic to Inorganic Material (subroutine DECBAL)
Purpose DECBAL adjusts the inorganic nitrogen and orthophosphorus state variables toaccount for decomposition of organic materials.
Method In subroutine NUTRX the total BOD decay for the time interval is used to computethe corresponding amounts of inorganic nitrogen and orthophosphorus produced by thedecay are determined as: DECNIT = BODOX*CVON (23) DECPO4 = BODOX*CVOP (24)
where: BODOX = total BOD decay (mg O/l per interval) CVON = stoichiometric conversion factor from mg oxygen to mg nitrogen CVOP = stoichiometric conversion factor from mg oxygen to mg phosphorus
The values for DECNIT and DECPO4 are passed to subroutine DECBAL. If ammonia issimulated, the value of DECNIT is added to the NH3 (TAM) state variable; if not,DECNIT is added to the NO3 state variable. If orthophosphorus is simulated, thevalue of DECPO4 is added to the PO4 state variable.
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4.2(3).7.3 Simulate Plankton Populations and Associated Reactions (Subroutine Group PLANK of Module RCHRES)
Purpose PLANK simulates phytoplankton, zooplankton, and/or benthic algae. Schematic View of Fluxes and Storages Figures 4.2(3).7.3-1 through 4.2(3).7.3-4 illustrate the fluxes and storages of thesix constituents which are introduced into the RCHRES modeling system in subroutinePLANK. In addition to these constituents, the state variables for dissolvedoxygen, biochemical oxygen demand, nitrate, total ammonia, and orthophosphorus arealso updated. If subroutine group PLANK is active (PLKFG = 1), dead refractoryorganics will automatically be simulated. The state variables for these organicsare ORN (dead refractory organic nitrogen), ORP (dead refractory organicphosphorus), and ORC (dead refractory organic carbon). The user must specifywhether or not phytoplankton, zooplankton, and/or benthic algae are simulated byassigning appropriate values to PHYFG, ZOOFG, and BALFG in the User's ControlInput. The state variable PHYTO represents the free floating photosynthetic algae,ZOO represents the zooplankton which feed on PHYTO, and BENAL is the state variablefor algae attached to the benthal surface. Subroutine group PLANK is a large and complex code segment. It uses twelvesubroutines to perform simulation of the three types of plankton. Longitudinaladvection of PHYTO and ZOO is performed by ADVPLK, a special advection routine forplankton. ORN, ORP, and ORC are advected by ADVECT. The sinking of PHYTO, ORN,ORP, and ORC is performed by subroutine SINK. The user controls the sinking rateof these constituents by assigning values to parameters PHYSET and REFSET in theUser's Control Input. PHYSET is the rate of phytoplankton settling, and REFSET isthe settling rate for all three of the dead refractory organic constituents.Advection and sinking are performed every interval.
Before ADVECT is called, PLANK sums the inputs of ORN, ORP, and ORC from upstreamreaches, tributary land areas, and atmospheric deposition:
INORG = IORG + SAREA*ADFX + SAREA*PREC*ADCN (1)
where: INORG = total input of organic to reach IORG = input of organic from upstream reaches and tributary land SAREA = surface area of reach ADFX = dry or total atmospheric deposition flux in mass/area per interval PREC = precipitation depth ADCN = concentration for wet atmospheric deposition in mass/volume
Atmospheric deposition inputs can be specified in two possible ways depending onthe form of the available data. If the deposition is in the form of a flux (massper area per time), then it is considered "dry deposition". If the deposition isin the form of a concentration in rainfall, then it is considered "wet deposition",and the program automatically combines it with the input rainfall time series to
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Figure 4.2(3).7.3-1 Flow diagram for phytoplankton in the PLANK section of the RCHRES Application Module
Figure 4.2(3).7.3-2 Flow diagram for dead refractory organics in the PLANK section of the RCHRES Application Module
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Figure 4.2(3).7.3-3 Flow diagram for zooplankton in the PLANK section of the RCHRES Application Module
Figure 4.2(3).7.3-4 Flow diagram for benthic algae in the PLANK section of the RCHRES Application Module
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compute the resulting flux. Either type of deposition data can be input as a timeseries, which covers the entire simulation period, or as a set of monthly valuesthat is used for each year of the simulation. The specific atmospheric depositiontime series for NUTRX are documented in the EXTNL table of the Time Series Catalogfor RCHRES, and are specified in the EXT SOURCES block of the UCI. The monthlyvalues are input in the MONTH-DATA block in the UCI.
The remainder of the processes modeled in PLANK are only performed when the averagedepth of water in the RCHRES is at least 2 inches. Experience has shown that thealgorithms used to represent these processes are not accurate for excessivelyshallow waters. If 2 inches or more of water is present in the RCHRES, PLANKperforms a series of operations which are necessary to determine the availabilityof light to support algal growth. First the light intensity at the RCHRES surfaceis calculated by the following equation:
INLIT = 0.97*CFSAEX*SOLRAD/DELT (1)
where: INLIT = light intensity immediately below water surface (langleys/min) 0.97 = correction factor for surface reflection (assume 3 percent) CFSAEX = input parameter that specifies the ratio of radiation at water surface to gage radiation values. This factor also accounts for shading of the water body, e.g. by trees and streambanks SOLRAD = solar radiation (langleys/interval) DELT = conversion from units of per interval to per minute
After the light intensity at the water surface has been calculated, PLANKdetermines the factors which diminish the intensity of light as it passes throughthe water. In addition to the natural extinction due to passage through water,extinction may result from interference caused by suspended sediment orphytoplankton. If SDLTFG is assigned a value of one, the contribution of totalsuspended sediment to light extinction is calculated as:
EXTSED = LITSED*SSEDT (2) where: EXTSED = increment to base extinction coefficient due to total suspended sediment (/ft) LITSED = multiplication factor to total suspended sediment conc. (supplied in User's Control Input) SSEDT = total suspended sediment (sand + silt + clay) (mg/l) The contribution of suspended phytoplankton to light extinction is determined bythe empirical relationship: EXTCLA = 0.00452*PHYCLA (3) where: EXTCLA = increment to base extinction coefficient due to phytoplankton (/ft) 0.00452 = multiplication factor to phytoplankton chlorophyll a concentration PHYCLA = phytoplankton concentration (micromoles/l of chlorophyll a)
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After values for INLIT, EXTSED, and EXTCLA have been calculated, PLANK callssubroutine LITRCH to determine the light correction factor to algal growth and theamount of light available to phytoplankton and benthic algae. Once thesecalculations have been completed, PLANK checks a series of flags to determine whichtypes of plankton are to be simulated. If PHYFG is assigned a value of one,simulation of phytoplankton is performed. Zooplankton are simulated if ZOOFG isgiven a value of one. Zooplankton simulation can be performed only if thephytoplankton section is active. Finally, a value of one for BALFG activatesbenthic algae simulation.
4.2(3).7.3.1 Advect Plankton (subroutine ADVPLK) Purpose ADVPLK performs the advection of phytoplankton and zooplankton. The normaladvection method (subroutine ADVECT) used in the RCHRES module assumes that eachconstituent concentration is uniform throughout the RCHRES. This assumption is notvalid for plankton. Both phytoplankton and zooplankton locate their breedinggrounds near the channel boundaries. Since the water near the boundaries movesdownstream much more slowly than the mean water velocity, the plankton populationshave a much longer residence time in the RCHRES than would be indicated by the meanflowtime. The geographical extent of the plankton breeding grounds is inverselyrelated to the flow rate. At low flows, large areas of slow moving waters whichare suitable for breeding exist along the channel boundaries. As flowratesincrease, more and more of these areas are subject to flushing. The specialadvection routine is critical to plankton simulation, because the only source ofplankton is within the reach network. Thus an upstream RCHRES with no planktoninflows can maintain a significant plankton population only if the growth rate ofplankton exceeds the rate at which plankton are advected out of the RCHRES. Sincebiological growth rates are typically much slower than "normal" advection rates,few free-flowing RCHRES's could maintain a plankton population without the use ofthe special advection routine. Method Figure 4.2(3).7.3-5 illustrates the relationships used to perform planktonadvection.
ADVPLK assumes that a certain concentration of plankton (STAY) is not subject toadvection, but any excess of organisms will be advected in the normal way. A smallpopulation (SEED) of plankton are never subject to advection, even during theperiods of greatest flow. The maximum concentration of plankton which is notsubject to advection (MXSTAY) occurs during low flow conditions. Each simulationinterval ADVPLK calculates STAY based on the values of these two parameters andOREF. OREF is the outflow rate at which STAY has a value midway between SEED andMXSTAY. First, the average flow rate through the RCHRES for the interval iscalculated: OFLO = (SROVOL + EROVOL)/DELTS (4)
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Figure 4.2(3).7.3-5 Relationship of parameters for special advection of plankton
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where: OFLO = average flow rate (ft3/s or m3/s) DELTS = number of seconds per interval SROVOL and EROVOL are as defined in Section 4.2(3).2 The concentration of plankton which is not subject to advection is then determined:
STAY = (MXSTAY - SEED)*(2.0**(-OFLO/OREF)) + SEED (5) where: STAY = plankton concentration not advected (mg/l) MXSTAY = maximum concentration not subject to advection SEED = concentration of plankton never subject to advection OREF = outflow rate at which STAY has a value midway between SEED and MXSTAY (ft3/s or m3/s) The amount of plankton not subject to advection is converted to units of mass (MSTAY) by multiplying STAY by the volume in the RCHRES at the start of theinterval (VOLS). The concentration of plankton which is advected is: PLNKAD = PLANK - STAY (6)
ADVPLK calls subroutine ADVECT to perform longitudinal advection of the quantityPLNKAD. The updated value of PLNKAD is then added to the amount of plankton whichdid not undergo advection to determine the concentration of plankton in the RCHRESat the end of the interval:
PLANK = PLNKAD + MSTAY/VOL (7) where: PLANK = concentration of plankton at end of interval PLNKAD = concentration of advected plankton which remain in RCHRES MSTAY = mass of plankton not advected VOL = volume in RCHRES at end of interval If the concentration of plankton in the RCHRES at the start of the interval is lessthan the value assigned to SEED, advection of plankton is not performed in theRCHRES, and the value of PLANK at the end of the interval is calculated as:
PLANK = (MSTAY + IPLANK)/VOL (8) where: IPLANK = mass of plankton which enters RCHRES during interval
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4.2(3).7.3.2 Calculate Light-related Information Needed for Algal Simulation (subroutine LITRCH) Purpose Subroutine LITRCH determines the light correction factor to algal growth and theamount of light available to phytoplankton and benthic algae. Method The overall light extinction factor for the interval is obtained by adding EXTSEDand EXTCLA to the base extinction coefficient (EXTB). The value of EXTB is assumedconstant for a particular RCHRES and must be assigned in the User's Control Input.The resulting sum (EXTCO) is used to calculate the euphotic depth, which is thedistance below the surface of the water body at which 1 percent of the lightincident on the surface is still available:
EUDEP = 4.60517/EXTCO (9)
where: EUDEP = euphotic depth (ft) EXTCO = total light extinction coefficient (/ft)
HSPF assumes that growth of algae occurs only in the euphotic zone (that is, thewater above euphotic depth). When EUDEP has been calculated, it is possible toassign a value to CFLIT, the light correction factor to algal growth. A value of1.0 is assigned to CFLIT if the calculated euphotic zone includes all the water ofthe RCHRES. CFLIT = EUDEP/AVDEPE, if the euphotic depth is less than the averagedepth of water (AVDEPE). CFLIT is used in subroutine ALGRO, to adjust the computedrate of algal growth.
Finally, the amount of light available to phytoplankton and benthic algae iscalculated. The equation used to calculate the amount of light available tophytoplankton assumes that all phytoplankton are at mid-depth in the RCHRES or themiddle of the euphotic zone, whichever is closer to the surface: PHYLIT = INLIT*Exp(-EXTCO*(.5*Min(EUDEP,AVDEPE))) (10) where: PHYLIT = light available to phytoplankton (langleys/min) INLIT = light available at water surface (langleys/min) EXTCO = light extinction coefficient (/ft) AVDEPE = average depth of water in the RCHRES (ft) Exp = Fortran exponential function Min = Fortran minimum function
The equation used to calculate the amount of light available to benthic algaeassumes that all benthic algae are at AVDEPE below the surface of the RCHRES: BALLIT = INLIT*Exp(-EXTCO*AVDEPE) (11)
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4.2(3).7.3.3 Simulate Phytoplankton (subroutine PHYRX) Purpose PHYRX simulates the algae which float in the water of a RCHRES. Because theseorganisms use energy from light to produce organic matter, they are called primaryproducers and are considered the first trophic level in the aquatic ecosystem. Thebiological activity of the ecosystem depends upon the rate of primary productionby these photosynthetic organisms. The activities of the phytoplankton are in turnaffected by the physical environment. Through the process of photosynthesis,phytoplankton consume carbon dioxide and release oxygen back into the water. Atthe same time, algal respiration consumes oxygen and releases carbon dioxide.Phytoplankton reduce the concentration of nutrients in the water by consumingphosphates, nitrate, and ammonia. Through assimilation these nutrients aretransformed into organic materials which serve as a food source for higher trophiclevels. A portion of the organic matter that is not used for food decomposes,which further affects the oxygen and nutrient levels in the water. Where thephytoplankton population has grown excessively, much of the available oxygen supplyof the water may be depleted by decomposition of dead algae and respiration. Inthis situation, phytoplankton place a serious stress upon the system. Approach To describe quantitatively the dynamic behavior of phytoplankton populations, anumber of assumptions must be made. PHYRX treats the entire phytoplanktonpopulation as if it were one species, and the mean behavior of the population isdescribed through a series of generalized mathematical formulations. While suchan approach obscures the behavior of individual species, the overall effect of thephytoplankton population on the water quality can be modeled with reasonableaccuracy. The HSPF system assumes that biomass of all types (phytoplankton, zooplankton,benthic algae, dead organic materials) has a consistent chemical composition. Theuser specifies the biomass composition by indicating the carbon:nitrogen:phosphorus ratio and the percent-by-weight carbon. This is done by assigningvalues to the following parameters: 1. CVBPC: number of moles of carbon per mole of phosphorus in biomass (default = 106)
2. CVBPN: number of moles of nitrogen per mole of phosphorus in biomass (default = 16)
3. BPCNTC: percentage of biomass weight which is carbon (default = 49) The algorithms used in PHYRX and its subroutines require that the phytoplanktonpopulation be expressed in units of micromoles of phosphorus per liter. PHYRXconverts the value for state variable PHYTO in milligrams biomass per liter intomicromoles phosphorus per liter and assigns this value to the internal statevariable STC (standing crop).
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PHYRX uses five routines to simulate phytoplankton. ALGRO computes unit growth andrespiration rates and determines the growth limiting factor for the phytoplankton.If the amount of growth exceeds the amount of respiration for the interval, GROCHKadjusts growth to account for nutrient limitations. PHYDTH calculates the amountof death occurring during the interval. State variables ORN, ORP, ORC, and BOD areupdated by ORGBAL to account for materials resulting from phytoplankton death.Finally, NUTRUP adjusts the values for PO4, NO3, and TAM (total ammonia) to accountfor uptake of nutrients by phytoplankton. In addition to these updates, thedissolved oxygen state variable is adjusted in PHYRX to account for the net effectof phytoplankton photosynthesis and respiration: DOX = DOX + (CVPB*CVBO*GROPHY) (12) where: CVPB = conversion factor from micromoles phosphorus to mg biomass CVBO = conversion factor from mg biomass to mg oxygen GROPHY = net growth of phytoplankton (micromoles phosphorus/l per interval)
After all the operations in PHYRX and its subroutines have been performed, thevalue of STC is converted back into units of milligrams biomass per liter andbecomes the updated value of PHYTO.
4.2(3).7.3.3.1 Calculate Unit Growth and Respiration Rates for Algae (subroutine ALGRO) Purpose ALGRO calculates the unit growth rate of algae based on light, temperature, andnutrients. Each time step, ALGRO determines the rate limiting factor for growth,and passes a label which identifies the limiting factor to the subroutinesresponsible for printed output. The labels and their meanings are as follows:
'LIT' Growth is light limited. 'NON' Insufficient nutrients are available to support growth. 'TEM' Water temperature does not allow algal growth. 'NIT' Growth is limited by availability of inorganic nitrogen. 'PO4' Growth is limited by availability of orthophosphorus. 'NONE' There is no limiting factor to cause less than maximal growth. 'WAT' Insufficient water is available to support growth.
ALGRO is also responsible for calculating the unit respiration rate for algae.This routine is used in the simulation of both phytoplankton and benthic algae.
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Approach ALGRO performs a series of initial checks to determine whether or not conditionsare suitable for growth during the interval. If the light intensity for theinterval is less than 0.001 langleys/min, insufficient light is available forgrowth, and growth is not calculated. Likewise, if the concentration of eitherinorganic nitrogen or orthophosphorus is less than 0.001 mg/l, no growth occurs.If these checks indicate that conditions are suitable for growth, ALGRO nextdetermines the effects of water temperature on the growth potential. Temperature Control The user specifies the temperature preferences of the algae by assigning values tothree parameters: TALGRL, TALGRM, and TALGRH. If the water temperature is lessthan the value assigned to TALGRL or greater than the value assigned to TALGRH, nogrowth occurs. For water temperatures between TALGRL and TALGRH, a correctionfactor to maximum growth rate (MALGR) is calculated. This correction factorincreases in value linearly from 0.0 at TALGRL to 1.0 at TALGRM. Thus, TALGRMspecifies the minimum temperature at which growth can occur at a maximum rate.ALGRO assumes that there is no temperature retardation of maximum growth rate fortemperatures between TALGRM and TALGRH. The temperature corrected maximum growthrate is: MALGRT = MALGR*TCMALG (13) where: MALGRT = temperature corrected maximum algal growth rate (/interval) MALGR = maximum unit growth rate for algae TCMALG = temperature correction to growth (TCMALG ranges between 0 and 1)
Once the temperature correction to potential growth rate has been made, ALGRO usesMonod growth kinetics with respect to orthophosphorus, inorganic nitrogen, andlight intensity to determine the actual growth rate. The procedure taken in ALGROis to consider each possible limiting factor separately to determine which onecauses the smallest algal growth rate during each simulation interval. This methoddoes not preclude that interactions between factors affect the actual growth rate;in cases where it has been established that there is such an interaction, as in theuptake of phosphate, the phenomena are included in the model. If none of thefactors considered is limiting, growth will be maximal and temperature dependent.
Phosphorus Limited Growth Algae are dependent upon uptake of orthophosphorus to provide the continual supplyof phosphorus necessary for ordinary cellular metabolism and reproductiveprocesses. In phosphorus-limited situations, the resultant growth rate has beenshown to be dependent not only on the concentration of phosphate ions, but onnitrate concentration as well (DiToro, et al., 1970). The phosphorus limitedgrowth rate is determined by: GROP = MALGRT*PO4*NO3/((PO4 + CMMP)*(NO3 + CMMNP)) (14)
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where: GROP = unit growth rate based on phosphorus limitation (/interval) MALGRT = temperature corrected maximum algal growth rate PO4 = orthophosphorus concentration (mg P/l) NO3 = nitrate concentration (mg N/l) CMMP = orthophosphorus Michaelis-Menten constant for phosphorus limited growth (mg P/l) (CMMP is defaulted to 0.015 mg P/l) CMMNP = nitrate Michaelis-Menten constant for phosphorus limited growth (mg N/l) (CMMNP is defaulted to 0.0284 mg N/l)
Nitrogen Limited Growth
Nitrogen is essential to algae for assimilation of proteins and enzymes. In theform of nitrate, nitrogen serves as the essential hydrogen acceptor in themetabolic pathways which enable organisms to grow. ALGRO allows for two differentsources of inorganic nitrogen. If ammonia is being simulated and a value of oneis assigned to the nitrogen source flag (NSFG), both ammonia and nitrate are usedby algae to satisfy their nitrogen requirements. Otherwise, only nitrate isconsidered in the kinetics formulations. High ratios of ammonia to nitrate havebeen found to retard algal growth. If a value of one is assigned to the ammoniaretardation flag (AMRFG), this phenomenon is simulated by the equation:
MALGN = MALGRT - 0.757*TAM + 0.051*NO3 (15)
where: MALGN = maximum unit growth rate corrected for ammonia retardation (/interval) MALGRT = temperature corrected maximum unit growth rate
Nitrogen limitation on growth is calculated by the equation: GRON = MALGN*MMN/(MMN + CMMN) (16) where: GRON = unit growth rate based on nitrogen limitation (per interval) MALGN = maximum unit growth rate (MALGN has the same value as MALGRT if AMRFG is set to zero) MMN = total pool of inorganic nitrogen considered available for growth CMMN = Michaelis-Menten constant for nitrogen limited growth (mg N/l) (CMMN is defaulted to 0.045 mg N/l)
Light Limited Growth The equation used to determine the limitation on growth rate imposed by lightintensity was derived by Dugdale and Macisaac (1971) based on uptake rates ofinorganic nitrogen under varying light intensities: GROL = MALGRT*LIGHT/(CMMLT + LIGHT) (17)
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where: GROL = unit growth rate based on light limitation (/interval) MALGRT = temperature corrected maximum unit growth rate (/interval) LIGHT = light intensity available to algae in RCHRES (langleys/min) CMMLT = Michaelis-Menten constant for light limited growth (langleys/min) (CMMLT is defaulted to 0.033 langleys/min)
Algal Respiration Algal respiration is dependent upon water temperature and is calculated by theequation: RES = ALR20*(TW/20.) (18) where: RES = unit algal respiration rate (/interval) ALR20 = unit respiration rate at 20 degrees C TW = water temperature (deg C)
4.2(3).7.3.3.2 Check Nutrients Required for Computed Growth (subroutine GROCHK)
GROCHK assures that a minimum concentration of 0.001 mg/l of each nutrient remainsin the RCHRES waters after growth occurs. If this condition is not satisfied, thecomputed growth rate is adjusted accordingly. Orthophosphorus and inorganicnitrogen are always considered as nutrients. If pH is simulated (PHFG = 1), theuser may specify that carbon dioxide concentration also be considered as a limitingnutrient by setting the value of DECFG equal to zero.
4.2(3).7.3.3.3 Calculate Phytoplankton Death (subroutine PHYDTH) Purpose PHYDTH calculates algal death each interval by using one of two unit death ratesspecified in the User's Control Input. ALDL, the low unit death rate, is used whenenvironmental conditions encourage sustained life. In situations where nutrientsare scarce or the phytoplankton population becomes excessive, ALDH, the high algaldeath rate, is used.
Method The high algal death rate, which has a default value of 0.01/hr, is used if any one of three conditions exists: 1. the concentration of PO4 is less than the value of parameter PALDH 2. the concentration of inorganic nitrogen is less than the value of parameter
NALDH 3. the concentration of phytoplankton is greater than the value of parameter
CLALDH
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Regardless of whether these tests indicate that ALDH or ALDL should be used, anadditional increment to death occurs if anaerobic conditions prevail during theinterval. The increment to death rate due to anaerobic conditions is determinedby the value of parameter OXALD. The amount of phytoplankton death which occursduring the interval is calculated as: DTHPHY = ALD*STC (19) where: DTHPHY = amount of phytoplankton death (micromoles P/l per interval) ALD = unit algal death rate determined by environmental conditions (/interval) STC = concentration of phytoplankton (micromoles P/l)
4.2(3).7.3.3.4 Perform Materials Balance for Transformation from Living to Dead Organic Material (subroutine ORGBAL) Purpose ORGBAL increments the concentrations of dead organics to account for planktondeath. Plankton death may either be algal death, zooplankton death, orphytoplankton ingested by zooplankton but not assimilated. In each case in whichORGBAL is called, the increments to ORP, ORN, ORC, and BOD are calculated in thesubroutine which makes the call and passed on to ORGBAL. ORGBAL is merely aservice program which performs the additions to these state variables.
4.2(3).7.3.3.5 Perform Materials Balance for Transformation from Inorganic to Organic Materials (subroutine NUTRUP) Purpose NUTRUP adjusts the concentrations of inorganic chemicals to account for net growthof algae. Net growth may be either positive or negative depending on the relativemagnitude of growth and respiration. The state variables which are updated byNUTRUP include PO4, NO3, TAM, and CO2.
Method The adjustments to PO4 and CO2 are straightforward. The PO4 state variable isalways updated; the CO2 state variable is only updated if pH is simulated (PHFG =1) and carbon dioxide is considered as a limiting nutrient (DECFG = 0). Adjustmentof the inorganic nitrogen state variables is more complex. If ammonia is notspecified as a source of inorganic nitrogen for growth (NSFG = 0), only the NO3state variable is updated to account for net growth. If ammonia is considered anutrient (NSFG = 1), negative net growth is accounted for by adding the total fluxof nitrogen to the TAM state variable. If net growth is positive, a portion of thenitrogen flux is subtracted from both the NO3 and TAM state variables. Therelative proportions of NO3 and TAM are governed by the value of parameter ALNPR,which is the fraction of nitrogen requirements for growth which are preferablysatisfied by nitrate.
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4.2(3).7.3.4 Simulate Zooplankton (subroutine ZORX) Purpose ZORX simulates the growth and death of zooplankton, and the resultant changes inthe biochemical balance of the RCHRES. Zooplankton play an important role indetermining the water quality of rivers and lakes. By feeding on the algal,bacterial, and detrital mass, they are a natural regulator in the aquaticenvironment. At the same time zooplankton are a source of food material for highertrophic levels such as fish. Through excretion, zooplankton provide nutrients forphytoplankton growth. HSPF is only concerned with those zooplankton which feed onphytoplankton, although in reality zooplankton may be herbivores, omnivores, orcarnivores. Schematic View of Fluxes and Storages Figure 4.2(3).7.3-3 illustrates the fluxes and storage of zooplankton modeled inZORX. In addition to zooplankton, the state variables for dissolved oxygen,biochemical oxygen demand, total ammonia, nitrate, orthophosphate, and refractoryorganics are also updated. Subroutine ZORX considers the following processes: 1. filtering and ingestion of phytoplankton by zooplankton 2. assimilation of ingested materials to form new zooplankton biomass 3. zooplankton respiration 4. inorganic and organic zooplankton excretion 5. zooplankton death
Filtering and Ingestion The amount of phytoplankton ingested per milligram zooplankton is calculated by theequation: ZOEAT = ZFIL20*(TCZFIL**(TW - 20.))*PHYTO (20) where: ZOEAT = unit ingestion rate (mg phyto/mg zoo per interval) ZFIL20 = zooplankton filtering rate at 20 degrees C (liters filtered/mg zoo per interval) TCZFIL = temperature correction coefficient for filtering TW = water temperature (deg C) PHYTO = phytoplankton concentration (mg phyto/l) The filtering rate is dependent upon water temperature and phytoplankton concen-tration. Rates for most biological activities double for every 10 degrees Centri-grade increase in temperature. The filtering rate meets this criterion if thedefault value of 1.17 is used for the temperature correction coefficient TCZFIL.
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When the phytoplankton biomass is below a critical concentration, the unitfiltering rate will be maximal and constant. As the phytoplankton biomassincreases above the critical concentration, the limiting rate is dependent oningestive and digestive capabilities, and not on the concentration of the foodsource. Under these conditions, the filtering rate decreases proportionally suchthat the algal biomass ingested remains constant at the value of the parameterMZOEAT, which is defaulted to 0.055 mg phytoplankton/mg zooplankton per hour. Thecode simulates this by reducing ZOEAT to MZOEAT, if Equation 20 gives a valuegreater than MZOEAT. HSPF assumes that the filtering activities of zooplankton are100 percent efficient; that is, the zooplankton ingest all of the food which iscontained in the water which they filter. The total amount of phytoplanktoningested by the zooplankton is calculated as: ZEAT = ZOEAT*ZOO (21) where: ZEAT = ingested phytoplankton (mg biomass/l per interval) ZOEAT = unit ingestion rate ZOO = zooplankton concentration (mg biomass/l) ZORX checks that the calculated amount of ingestion does not reduce thephytoplankton population to less than 0.0025 micromoles of phosphorus per liter;if it does, the ingestion rate is adjusted to maintain a phytoplanktonconcentration at this level.
Assimilation Assimilation is the process by which ingested phytoplankton are converted to newzooplankton mass. The process of assimilation is never 100 percent efficient inbiological systems. Unassimilated food is excreted as organic and inorganic wasteproducts. Zooplankton assimilation efficiency is dependent upon quality andconcentration of food. High quality food is assimilated at high efficiency,whereas low quality food is mostly excreted as waste resulting in low assimilationefficiency. The relationship between food concentration and assimilationefficiency is more complex. If the concentration of available food and thefiltering rate of an organism are such that the organism ingests more food than canbe readily used for growth and metabolism, the organism's assimilation efficiencydecreases. The model represents the effect of food quality and concentration onassimilation as shown in Figure 4.2(3).7.3-6. The quality of the zooplankton food is assigned in the User's Control Input by theparameter ZFOOD. Three qualities of food are allowed. From these, one type mustbe chosen to represent the overall food source available to the zooplankton: 1 = high quality food ZFOOD = 2 = medium quality 3 = low quality Depending on the value assigned to ZFOOD, the assimilation efficiency ZEFF iscalculated by one of the following equations:
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Figure 4.2(3).7.3-6 Zooplankton assimilation efficiency
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IF ZFOOD = 1 THEN ZEFF = -0.06*PHYTO + 1.03 (22) IF ZEFF > 0.99 THEN ZEFF = 0.99 IF ZFOOD = 2 THEN ZEFF = -0.03*PHYTO + 0.47 IF ZEFF < 0.20 THEN ZEFF = 0.20
IF ZFOOD = 3 THEN ZEFF = -0.013*PHYTO + 0.17 IF ZEFF < 0.03 THEN ZEFF = 0.03 These equations are extrapolations from research on Daphnia (Schindler, 1968). Thecorrections to ZEFF set reasonable upper or lower limits on efficiency forassimilating each type of food. The mass of ingested phytoplankton assimilated byzooplankton is calculated as: ZOGR = ZEFF*ZEAT (23) where: ZOGR = zooplankton growth (mg biomass/l per interval) ZEFF = assimilation efficiency (dimensionless) ZEAT = ingested phytoplankton (mg biomass/l per interval)
Respiration Respiration is the biochemical process by which organic molecules are broken down,resulting in a release of energy which is essential for cellular and organismalactivities. The oxidized molecules may either be carbohydrates and fats storedwithin the organism or food passing through the organism's digestive system. Ineither case, the end result of respiration is a decrease in zooplankton mass anda subsequent release of inorganic nutrients. The equation governing zooplanktonrespiration is:
ZRES = ZRES20*(TCZRES**(TW - 20.))*ZOO (24)
where: ZRES = zooplankton biomass respired (mg zoo/l per interval) ZRES20 = respiration rate at 20 degrees C (default= 0.0015/hr) TCZRES = temperature correction factor for respiration (default = 1.07) ZOO = zooplankton (mg biomass/l)
Excretion Products Excretion is the ingested food which is not assimilated by the zooplankton. Thesewaste products contain both refractory and nonrefractory materials. The amount ofrefractory organic excretion is calculated as: ZREFEX = REFR*ZEXMAS (25)
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where: ZREFEX = refractory organic material excreted by zooplankton (mg refractory biomass/l per interval) ZEXMAS = total mass of zooplankton excretion (ZEXMAS is the difference between ZEAT and ZOGR) REFR = fraction of biomass which is refractory (REFR is the complement of parameter NONREF)
The nonrefractory portion of the excretion is released to the water in the form ofinorganic nutrients and undegraded BOD materials. The relative abundance of thematerials is dependent upon the unit ingestion rate of the zooplankton (ZOEAT).At higher ingestion rates, a larger fraction of the nonrefractory excretion is notdecomposed and is released as BOD materials. In the model, the parameter ZEXDELis the fraction of nonrefractory excretion which is immediately decomposed andreleased to the water as inorganic nutrients when the unit ingestion rate of thezooplankton is maximal. If the unit ingestion rate is less than maximal, the modelassumes that all the nonrefractory excretion is released to the water as inorganicnutrients. Thus, the amount of excretion released as inorganic materials is: ZINGEX = ZEXDEC*(ZEXMAS - ZREFEX) (26) where: ZINGEX = amount of biomass decomposed to inorganic excretion (mg biomass/l per interval) ZEXDEC = fraction of nonrefractory inorganic excretion (ZEXDEC = 1 for ZOEAT <= MZOEAT and ZEXDEC = ZEXDEL for ZOEAT > MZOEAT. Value of ZOEAT is that given by equation 20, i.e., prior to adjustment.)
The remaining portion of the excretion is considered to be BOD materials, and iscalculated as:
ZNRFEX = ZEXMAS - ZREFEX - ZINGEX (27)
where: ZNRFEX = amount of biomass released as nonrefractory organic excretion (mg biomass/l per interval)
Death Zooplankton death is the termination of all ingestion, assimilation, respiration,and excretion activities. After death, zooplankton contribute both refractory andnonrefractory materials to the system. Under aerobic conditions, the mass rate ofzooplankton death is determined by multiplying the natural zooplankton death rate,ZD, by the zooplankton concentration. If anaerobic conditions exist, an increasein zooplankton death rate is modeled by adding the value of the anaerobic deathrate parameter, OXZD, to ZD. The default value of ZD is 0.0001/hr and that of OXZDis 0.03/hr.
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Materials Balance for Related Constituents Research has shown that 1.10 mg of oxygen are consumed for every gram ofzooplankton mass which is respired (Richman, 1958). The DOX state variable isreduced accordingly in ZORX. If there is not sufficient oxygen available tosatisfy respiration requirements, the deficit is added to the BOD state variable,and DOX is set equal to zero. ZORX makes use of subroutine DECBAL to update the state variables TAM, NO3, and PO4to account for additions from zooplankton respiration and inorganic excretion. Theamount of inorganic constituents produced by these two processes is calculated bythe following equations: ZNIT = (ZINGEX + ZRES)*CVBN (28) ZPO4 = (ZINGEX + ZRES)*CVBP ZCO2 = (ZINGEX + ZRES)*CVBC where: ZNIT = increment to TAM or NO3 state variable (mg N/l per interval) ZPO4 = increment to PO4 state variable (mg P/l per interval) ZCO2 = increment to CO2 state variable (mg C/l per interval) ZINGEX = biomass decomposed to inorganic excretion (mg biomass/l per interval) ZRES = biomass respired by zooplankton (mg biomass/l per interval) CVBN = conversion factor from biomass to equivalent nitrogen CVBP = conversion factor from biomass to equivalent phosphorus CVBC = conversion factor from biomass to equivalent carbon
If ammonia is simulated, the inorganic nitrogen released is added to the TAMvariable; otherwise, it is added to the NO3 variable. The value of ZCO2 iscomputed for use in subroutine group PHCARB if pH simulation is performed.Finally, ZORX calls subroutine ORGBAL to update the state variables for ORN, ORP,ORC, and BOD to account for additions from zooplankton death and organic excretion.The amounts of organic constituents produced by these processes are calculated as: ZORN = ((REFR*ZDTH) + ZREFEX)*CVBN (29) ZORP = ((REFR*ZDTH) + ZREFEX)*CVBP ZORC = ((REFR*ZDTH) + ZREFEX)*CVBC ZBOD = (ZDTH*CVNRBO) + (ZNRFEX*CVBO) where: ZORN = increment to ORN state variable (mg N/l per interval) ZORP = increment to ORP state variable (mg P/l per interval) ZORC = increment to ORC state variable (mg C/l per interval) ZBOD = increment to BOD state variable (mg O/l per interval REFR = refractory fraction of biomass ZDTH = zooplankton death (mg biomass/l per interval) ZREFEX = refractory organic excretion (mg biomass/l per interval) ZNRFEX = nonrefractory organic excretion (mg biomass/l per interval) CVBO = conversion from biomass to equivalent oxygen CVNRBO = conversion from nonrefractory biomass to equivalent oxygen, times NONREF
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4.2(3).7.3.5 Simulate Benthic Algae (subroutine BALRX) Purpose BALRX simulates those algae in the RCHRES which are attached to rocks or otherstable structures. In free flowing streams, large diurnal fluctuations of oxygencan be attributed to benthic algae. During the sunlight hours, if sufficientnutrients exist to support photosynthesis, oxygen is produced in such largequantities that supersaturation often occurs. However, at night, whenphotosynthesis cannot occur, the benthic algae can exert a significant demand onthe oxygen supply of the RCHRES due to respiratory requirements. Benthic algaeinfluence the nutrient balance of the RCHRES by their extraction of nutrients forgrowth. Approach The growth and death of benthic algae are modeled in much the same manner as theirfree floating relatives, the phytoplankton. In fact, four of the five subroutinesthat are used for phytoplankton simulation are also used in the benthic algaesimulation. These routines are ALGRO, GROCHK, ORGBAL, and NUTRUP. There are twomajor differences in modeling the two types of algae. First, since the benthicalgae are attached to materials in the RCHRES, they are not subject to longitudinaladvection. Second, the manner in which death of benthic algae is modeled issufficiently different from the method used for phytoplankton that a specialroutine, BALDTH, is used. Within BALRX benthic algae are in units of micromolesphosphorus per liter so that the benthic algae simulation can take advantage of thesame subroutines used by PHYRX. In order to obtain these units, the followingconversion is performed:
BAL = BENAL*DEPCOR/CVPB (30)
where: BAL = benthic algae (micromoles phosphorus/l) BENAL = benthic algae (mg biomass/m2) CVPB = conversion factor from micromoles phosphorus to mg biomass DEPCOR = conversion from square meters to liters based on average depth of water in RCHRES during the interval (DEPCOR is computed in RQUAL)
Net Growth Unit growth and respiration rates for benthic algae are calculated by subroutineALGRO. The user has the option of multiplying either of these rates by a constantfactor if there is evidence that the benthic algae population does not exhibit thesame growth and respiration rates as the phytoplankton population. Thus, netgrowth rate is calculated as: GROBAL = (GRO*CFBALG - RES*CFBALR)*BAL (31)
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where: GROBAL = net growth rate of benthic algae (micromoles P/l per interval GRO = unit growth rate as calculated in subroutine ALGRO CFBALG = ratio of benthic algae to phytoplankton growth rates under identical growth conditions (default = 1.0) RES = unit respiration rate as calculated in subroutine ALGRO CFBALR = ratio of benthic algae to phytoplankton respiration rates (default = 1.0) BAL = benthic algae concentration (micromoles P/l)
After GROBAL has been calculated, subroutine GROCHK is called to assure that thecalculated growth does not reduce any nutrient to a concentration less than 0.001mg/l. If it does, GROBAL is adjusted to satisfy this requirement.
Death of Benthic Algae Subroutine BALDTH calculates the amount of benthic algae death and passes thisinformation back to BALRX (variable DTHBAL). BALRX updates the state variable BALto account for net growth and death. The value of BAL is not allowed to fall below0.0001 micromoles of phosphorus per square meter.
Materials Balance for Related Constituents The DOX state variable is updated to account for the net effect of benthic algaephotosynthesis and respiration according to the following equation: DOX = DOX + (CVPB*CVBO*GROBAL) (32) where: DOX = concentration of dissolved oxygen (mg/l) CVPB = conversion factor from micromoles phosphorus to mg biomass CVBO = conversion factor from mg biomass to mg oxygen GROBAL = net growth of benthic algae (micromoles phosphorus/l per interval)
The additions to ORN, ORP, ORC, and BOD resulting from benthic algae death arecalculated as: BALORN = REFR*DTHBAL*CVBPN*.014 (33) BALORP = REFR*DTHBAL*.031 BALORC = REFR*DTHBAL*CVBPC*.012 BALBOD = CVNRBO*CVPB*DTHBAL
Subroutine Group PLANK
252
where: BALORN = increment to ORN state variable (mg N/l per interval) BALORP = increment to ORP state variable (mg P/l per interval) BALORC = increment to ORC state variable (mg C/l per interval) BALBOD = increment to BOD state variable (mg O/l per interval) REFR = refractory fraction of biomass DTHBAL = benthic algae death (micromoles P/l per interval) CVNRBO = conversion from mg biomass to equivalent mg oxygen demand (allowing for refractory fraction) CVPB = conversion from micromoles phosphorus to mg biomass CVBPN = conversion from micromoles phosphorus to micromoles nitrogen CVBPC = conversion from micromoles phosphorus to micromoles carbon
When BALORN, BALORP, BALORC, and BALBOD have been evaluated, subroutine ORGBAL iscalled to perform the actual increments to the appropriate state variables.Finally, subroutine NUTRUP is called to update the inorganic state variables toaccount for net growth.
External Units The output values for benthic algae are in units of milligrams biomass per squaremeter and micrograms chlorophyll a per square meter.
4.2(3).7.3.5.1 Calculate Benthic Algae Death (subroutine BALDTH) Purpose BALDTH calculates algal death each interval by using one of two unit death ratesspecified in the User's Control Input. ALDL, the low unit death rate, is used whenenvironmental conditions encourage sustained life; in situations where nutrientsare scarce or the benthic algae population becomes excessive, ALDH, the high algaldeath rate, is used. Method The high algal death rate, which has a default value of 0.01/hr, is used if any oneof three conditions exists: 1. the concentration of PO4 is less than the value of parameter PALDH 2. the concentration of inorganic nitrogen is less than the value of parameter
NALDH 3. the areal density of benthic algae is greater than the value of parameter MBAL
Subroutine Group PLANK
253
Regardless of whether these tests indicate that ALDH or ALDL (default equals0.001/hr) should be used, an additional increment to death occurs if anaerobicconditions are prevalent during the interval. The increment to death rate due toanaerobic conditions is determined by the value of parameter OXALD. When thebenthic algae population grows to a size greater than that which may be supportedon the bottom surface, algae begin to break away from the bottom, a phenomenonknown as sloughing. Whenever the population calculated exceeds the maximumallowable bottom density (MBAL), the sloughing process removes the excess algae.The amount of benthic algae death which occurs during the interval is calculatedas: DTHBAL = (ALD*BAL) + SLOF (34) where: DTHBAL = amount of benthic algae death (micromoles P/l per interval) ALD = unit algal death rate determined by environmental conditions (/interval) BAL = concentration of benthic algae (micromoles P/l) SLOF = amount of benthic algae sloughed (micromoles P/l per interval)
Subroutine Group PHCARB
254
4.2(3).7.4 Simulate pH, Carbon Dioxide, Total Inorganic Carbon, and Alkalinity (Subroutine Group PHCARB of Module RCHRES)
Purpose
PHCARB calculates the pH of the water within a RCHRES. The primary value of pH isas an indicator of the chemical environment of the system. Under normalcircumstances, pH is near neutral, that is, near seven. Most life sustainingprocesses are impaired at extremes of pH.
Method
Figure 4.2(3).7.4-1 illustrates the fluxes and storages of constituents introducedin this section. Determination of pH requires simulation of alkalinity, carbondioxide, and total inorganic carbon. Within PHCARB, state variables for alkalinity(ALK), carbon dioxide (CO2), and total inorganic carbon (TIC) are expressed asmolar concentrations to correspond to the equilibrium expressions necessary todetermine pH. The conversion from mg/l to moles/l takes place after longitudinaladvection has been considered. Externally, ALK, CO2, and TIC are expressed inmg/l.
Alkalinity
Alkalinity is defined as the amount of acid required to attain a pH value equal tothat of a total inorganic carbon molar solution of H2CO3. This pH value is near4.5, which is approximately the lowest pH value tolerated by most forms of aquaticlife. Alkalinity is interpreted as the acid neutralizing capacity of naturalwaters.
Alkalinity is simulated as a conservative constituent, in module section CONS.Parameter ALKCON, in the User's Control Input for PHCARB, specifies whichconservative substance is alkalinity. For example, if ALKCON = 3 then subroutinePHCARB will assume that alkalinity is the 3rd conservative constituent.
Carbon Dioxide and Total Inorganic Carbon
HSPF assumes that changes in the TIC concentration occur only as changes in CO2concentration. Thus, the sources of TIC are:
1. carbon dioxide invasion (input) from the atmosphere 2. zooplankton respiration 3. carbon dioxide released by BOD decay 4. net growth of algae (if negative) 5. benthal release of carbon dioxide (if BENRFG = 1)
Subroutine Group PHCARB
255
Figure 4.2(3).7.4-1 Flow diagram of inorganic carbon in the PHCARB group of the RCHRES Application Module
Subroutine Group PHCARB
256
The sinks of TIC are:
1. carbon dioxide release to the atmosphere 2. net growth of algae (if positive)
All of these quantities except carbon dioxide invasion are calculated in othersubroutines and passed into PHCARB.
Carbon Dioxide Invasion
In order to calculate carbon dioxide invasion, the saturation concentration of CO2must be determined. First, Henry's constant for CO2, defined as the molarconcentration of atmospheric CO2 divided by the partial pressure of CO2, iscalculated by the equation:
S = 10.**(2385.73/TWKELV - 14.0184 + 0.0152642*TWKELV) (1) where: S = Henrys's constant for CO2 TWKELV = temperature of water (deg K) Using Henry's constant, the saturation concentration of CO2 is calculated as:
SATCO2 = 3.16E-04*CFPRES*S (2)
where: SATCO2 = saturation concentration of CO2 (moles CO2-C/l) CFPRES = correction to atmospheric pressure resulting from elevation difference (calculated in the Run Interpreter) S = Henry's constant for CO2
The carbon dioxide invasion is then calculated by the following equation:
ATCO2 = KCINV*(SATCO2 - CO2) (3)
where: ATCO2 = carbon dioxide invasion expressed as moles CO2-C/l per interval KCINV = carbon dioxide invasion coefficient (/interval) SATCO2 = saturation concentration of CO2 (moles CO2-C/l) CO2 = concentration of CO2 after longitudinal advection (moles CO2-C/l)
A positive value for ATCO2 indicates addition of CO2 to the water; a negative valueindicates a release of CO2 from water to the atmosphere. The value of KCINV isdependent upon the value calculated for KOREA, the oxygen reaeration coefficient,in subroutine group OXRX:
Subroutine Group PHCARB
257
KCINV = CFCINV*KOREA (4)
where: KCINV = carbon dioxide invasion coefficient (/interval) CFCINV = parameter specifying ratio of CO2 invasion rate to O2 reaeration rate KOREA = oxygen reaeration coefficient (/interval)
Net Carbon Dioxide Flux
The net carbon dioxide flux is determined by the following equation:
DELTCD = ATCO2 + (ZCO2 - ALGCO2 + DECCO2 + BENCO2)/12000. (5)
where: DELTCD = net CO2 flux (moles CO2-C/l per interval) ATCO2 = CO2 invasion (moles CO2-C/l per interval) ZCO2 = CO2 released by zooplankton excretion and respiration (mg CO2-C/l per interval) ALGCO2 = CO2 flux due to net growth of algae (mg CO2-C/l per interval) DECCO2 = CO2 released by BOD decay (mg CO2-C/l per interval) BENCO2 = benthal release of CO2 (mg CO2-C/l per interval) 12000. = conversion from mg CO2-C/l to moles CO2-C/l
If DECFG, the flag which decouples CO2 from the algal simulation, has a value ofone, ALGCO2 has a value of zero in this equation. Benthal release rates for bothaerobic and anaerobic conditions must be included in the User's Control Input ifbenthal release of CO2 is simulated. Since HSPF assumes that changes in totalinorganic carbon concentration only occur as changes in carbon dioxide, the updateto the TIC state variable for each simulation interval is:
TIC = TIC + DELTCD (6)
where: TIC = total inorganic carbon (moles C/l)
The Carbonate System
The value of pH is controlled by the carbonate system. There are three species ofimportance to the system: [H2CO3*], [HCO3], and [CO3]. [H2CO3*] is defined as thesum of [H2CO3] and [CO2]; for modeling purposes [H2CO3] is negligible relative to[CO2]. The carbonate system can be described by the following equations:
[H]*[HCO3]/[H2CO3*] = K1EQU (7) [H]*[CO3]/[HCO3] = K2EQU [H]*[OH] = KWEQU [H2CO3*] + [HCO3] + [CO3] = TIC [HCO3] + 2*[CO3] + [OH] - [H] = ALK
Subroutine Group PHCARB
258
where: [H] = hydrogen ion concentration (moles/l) [OH] = hydroxide ion concentration (moles/l) [CO3] = carbonate ion concentration (moles/l) [HCO3] = bicarbonate ion concentration (moles/l) [H2CO3*] = carbonic acid/carbon dioxide concentration (moles/l) K1EQU = first dissociation constant for carbonic acid K2EQU = second dissociation constant for carbonic acid KWEQU = ionization product of water
The five unknown values ([H2CO3*], [HCO3], [CO3], [H], [OH]) can be determined whenK1EQU, K2EQU, KWEQU, TIC, and ALK are known. K1EQU, K2EQU, and KWEQU are allfunctions of water temperature and are evaluated by the following equations:
K1EQU = 10.**(-3404.71/TWKELV + 14.8435 - 0.032786*TWKELV) (8) K2EQU = 10.**(-2902.39/TWKELV + 6.4980 - 0.02379*TWKELV) KWEQU = 10.**(-4470.99/TWKELV + 6.0875 - 0.01706*TWKELV)
where: TWKELV = absolute temperature of water (deg K)
Calculation of pH and CO2
Once values have been determined for K1EQU, K2EQU, KWEQU, TIC, and ALK, anequilibrium equation can be developed for hydrogen ion concentration ([H]). Thefive equations representing the carbon system (Equation 7) can be reduced to afourth order polynomial expression:
[H]**4 + COEFF1*([H]**3) + COEFF2*([H]**2) + COEFF3*[H] + COEFF4 = 0 (9)where: COEFF1 = ALK + K1EQU COEFF2 = -KWEQU + ALK*K1EQU + K1EQU*K2EQU - TIC*K1EQU COEFF3 = -2.*K1EQU*K2EQU*TIC - K1EQU*KWEQU + ALK*K1EQU*K2EQU COEFF4 = -K1EQU*K2EQU*KWEQU [H] = hydrogen ion concentration (moles/l)
The solution of this equation is performed by subroutine PHCALC. Based on thehydrogen ion concentration calculated in PHCALC, the concentration of CO2 isrecalculated as:
CO2 = TIC/(1. + K1EQU/HPLUS + K1EQU*K2EQU/(HPLUS**2)) (10) where: CO2 = carbon dioxide concentration (moles C/l) TIC = total inorganic carbon concentration (moles C/l)
Subroutine Group PHCARB
259
K1EQU = first dissociation constant of carbonic acid K2EQU = second dissociation constant of carbonic acid HPLUS = hydrogen ion concentration (moles H/l)
Finally, the units of TIC, CO2, and ALK are converted back to mg/l for use outsideof PHCARB.
4.2(3).7.4.1 Calculate pH (subroutine PHCALC)
PHCALC uses the Newton-Raphson method to solve the fourth order polynomialexpression for the hydrogen ion concentration (Equation 9). The user specifies themaximum number of iterations performed by assigning a value to parameter PHCNT.PHCALC continues the iteration process until the solutions for pH concentration oftwo consecutive iterations differ by no more than one tenth of a pH unit. If thesolution technique does not converge within the maximum allowable number ofiterations, PHCALC passes this information back to PHCARB by assigning a value ofzero to CONVFG. An error message is printed and then PHCALC is called again, torepeat the unsuccessful iteration process. This time, the "debug flag" (PHDBFG)is set ON so that, for each iteration, PHCALC will print information which willhelp the user track down the source of the problem.
Module COPY
260
4.2(11) Copy Time Series (Utility Module COPY)
This utility module is used to copy one or more time series from a source specifiedin the EXT SOURCES or NETWORK Block of the User's Control Input (UCI), to a targetspecified in the NETWORK or EXT TARGETS Block (Part F, Section 4.6). To operate the COPY module, the user must specify the time interval used in theinternal scratch pad (INDELT) and the number of point-valued and mean-valued timeseries to be copied (NPT and NMN in Part F, Section 4.4(11).1). Up to 20point-valued and/or 20 mean-valued time series may be copied in a single operation.
Module TSGET transfers the time series from the source(s), which may be eitherexternal (e.g. WDM or DSS data set or sequential file) or the output(s) from oneor more preceding operations, to the INPAD. DSS Data sets with time steps otherthan the internal scratch pad time interval (INDELT) will be automaticallyaggregated or disaggregated. Data from sequential files must be at the INDELTinterval. It also automatically alters the "kind" of time series, if appropriate,and can multiply each value by a user-specified factor.
Module TSPUT then transfers the time series from the INPAD to the target which,again, can be either external or internal. The work performed is a mirror imageof that done by TSGET; time series can be aggregated/disaggregated and/ortransformed in the same way.
Module COPY is typically used to transfer time series, such as precipitation andpotential evapotranspiration data, from a sequential file (e.g., ASCII data) to adata set in the WDM file or DSS. Thereafter, when these data are used as inputsto simulation operations, they are read directly from the WDM or DSS.
COPY can also be used to change the "kind" and/or interval of one or more timeseries. For example, a WDM data set containing hourly precipitation data could beinput to COPY and the output stored in another WDM data set with a daily time step.The data would automatically be aggregated.
Module PLTGEN
261
4.2(12) Prepare Time Series for Display on a Plotter (Utility Module PLTGEN)
This utility module prepares one or more time series for simultaneous display ona plotter. As with the COPY module (Section 4.2(11)), the user must specify theinput(s) (sources), using entries in the EXT SOURCES or NETWORK Blocks in hiscontrol input (UCI). The internal time-step and the number of point- and/ormean-valued time series to be displayed must also be specified.
TSGET transfers the time series from the source(s) to the INPAD (as in COPY).PLTGEN then outputs these data to a plot file (PLOTFL). This is a sequential file;the first 25 records contain general information, such as the plot heading, numberof curves to be plotted, scaling information, etc. Each subsequent recordcontains:
Cols Contents 1 - 4 Identifier (first 4 characters of title) 6 - 10 Year 11 - 13 Month 14 - 16 Day 17 - 19 Hour 20 - 22 Minute25 - 36 Value for curve 1, for this date/time 39 - 50 Value for curve 2, for this date/time etc (repeats until data for all curves are supplied)
Format: A4,1X,I5,4I3,10(2X,G12.5)
The time resolution of the PLOTFL is the INDELT of the run, an integer multipleof the INDELT which is also evenly divisible into one day, one month, or one year.
A PLOTFL may contain only records greater than a certain threshold value, THRESH,or during a certain span of time specified in the Special Actions Block.
The contents of a sample PLOTFL are listed in Figure 4.2(12)-1. To keep thelisting short, only the first four values have been included
A plot file is intended to be read by a stand-alone plot program, which translatesits contents into information used to drive a plotting device. Alternative usesof a PLOTFL are:
1. To display one or more time series in printed form. For example: To examinethe contents of a data set in the WDM file, input it to PLTGEN and list thecontents of the PLOTFL on a line printer or terminal.
2. To transfer time series to some other stand-alone program, such as aspreadsheet or graphical display program. For example, one could specify thecontents of a PLOTFL as input to a program which performs statistical analysisor computes cross correlations between time series.
Module PLTGEN
262
Plot HSPF FILE FOR DRIVING SEPARATE PLOT PROGRAMPlot Time interval: 30 mins Last month in printout year: 9Plot No. of curves plotted: Point-valued: 2 Mean-valued: 0 Total: 2 Plot Label flag: 0 PIVL: 1 IDELT: 30Plot Plot title: Plot of reservoir flowratesPlot Y-axis label: Flow (ft3/sec) Plot Scale info: Ymin: .00000E+00Plot Ymax: 1000.0Plot Time: 48.000 intervals/inch Plot Data for each curve (Point-valued first, then mean-valued):Plot Label LINTYP INTEQ COLCOD TRAN TRANCOD Plot Inflow 0 0 1 SUM 1 Plot Outflow 0 0 1 SUM 1 PlotPlotPlotPlotPlotPlotPlotPlotPlot Time series (pt-valued, then mean-valued): PlotPlot Date/time ValuesPlotPlot 1974 5 31 24 0 .00000E+00 1.0000Plot 1974 6 1 0 30 .82838 1.0000Plot 1974 6 1 1 0 1.5071 1.0000Plot 1974 6 1 1 30 2.0631 1.0000
Figure 4.2(12)-1 Sample PLOTFL (showing four time points)
Module DISPLY
263
4.2(13) Display Time Series in a Convenient Tabular Format (Utility Module DISPLY)
The purpose of this module is to permit any time series to be displayed (at avariety of time intervals) in a tabular format. Sample outputs are shown inFigures 4.2(13)-1 through -3. Salient features of this module are:
1. Any time series (input or computed) can be displayed. The user specifies thetime series in the EXT SOURCES or NETWORK Block, as with any other module.
2. As with any other module, the data are first placed in the INPAD, by moduleTSGET. At this point they are at the time interval specified for thisoperation in the OPN SEQUENCE Block (INDELT). This might have involvedaggregation or disaggregation if the data were brought in from the WDM file.In general, INDELT can be any of the 19 HSPF supported time steps, rangingfrom 1 minute to 1 day.
3. The user can elect to display the data in a "long-span table" or a "short-spantable". The term "span" refers to the period covered by each table. A short-span table (Figures 4.2(13)-1 and -2) covers a day or a month at a time anda long-span table (Figure 4.2(13)-3) covers a year.
4. The user selects the time-step for the individual items in a short-spandisplay (the display interval) by specifying it as a multiple (PIVL) ofINDELT. For example, the data in Figure 4.2(13)-1 are displayed at aninterval of 5 minutes. This could have been achieved with the followingscenarios:
INDELT PIVL
5 min 1 1 min 5
If the display interval is less than one hour, one hours worth of data aredisplayed on one printed "row" (Figure 4.2(13)-1). The number of items in arow depends on their interval (e.g., 60 for one minute, 12 for 5 minutes, 2for 30 mins.). A "row" may actually occupy up to 5 physical lines of printoutbecause a maximum of 12 items is placed on a line. The entire table spans oneday.
Module DISPLY
264
If the display interval is greater than or equal to one hour, a day's worthof data are displayed on one "row" (Figure 4.2(13)-2). Again, the number ofitems in a row depends on the display interval. In this case the entire tablespans a month.
5. A long-span table always covers one year; the display interval for theindividual items in the table is one day (Figure 4.2(13)-3). The user canselect the month which terminates the display (December in the example) sothat the data can be presented on a calendar year, water year or some otherbasis.
6. For the purpose of aggregating the data from the interval time step (INDELT)to the display interval, day-value, month-value, or year-value, one of five"transformation codes" can be specified:
Code Meaning SUM Sum of the data AVER Average of the data MAX Take the max of the values at the smaller time step MIN Take the minimum LAST Take the last of the values belonging to the shorter time step
SUM is appropriate for displaying data like precipitation; AVER is useful fordisplaying data such as temperatures.
7. The module incorporates a feature designed to permit reduction of the quantityof printout produced when doing short-span displays. If the "row-value"("hour-sum" in Figure 4.2(13)-1; "day-average" in Figure 4.3(13)-2) is lessthan or equal to a threshold value, printout of the entire row is suppressed.The default threshold is 0.0. Thus, in Figure 4.2(13)-1; data for dry hoursare not printed.
8. The user can also specify the following:
a. The number of decimal digits to use in a display.
b. A title for the display.
c. A linear transformation, to be performed on the data when they are at theINDELT time interval (i.e. before module DISPLY performs anyaggregation). By default, no transformation is performed.
Module DISPLY
265
WDM
39 P
reci
p. (
in/1
00)
Summ
ary
for
DAY
197
6/10
/4Da
te i
nter
val:
5
mins
HOUR
SUM
Inte
rval
Num
ber
12
34
56
78
910
1112
3:
2.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
0.0
1.0
4:3.
00.
00.
01.
00.
00.
00.
01.
00.
00.
01.
00.
00.
0
5:5.
00.
00.
00.
00.
01.
00.
00.
00.
00.
00.
02.
02.
0
6:6.
01.
01.
02.
01.
01.
00.
00.
00.
00.
00.
00.
00.
0
7:3.
01.
00.
00.
00.
00.
01.
00.
00.
00.
00.
00.
01.
0
8:3.
00.
00.
01.
00.
00.
01.
00.
00.
00.
01.
00.
00.
0
9:3.
00.
00.
01.
00.
00.
00.
00.
01.
00.
00.
01.
00.
0
10:
3.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
1.0
11:
3.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
1.0
0.0
12:
4.0
1.0
1.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
13:
3.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
14:
2.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
0.0
15:
4.0
0.0
0.0
1.0
0.0
1.0
0.0
0.0
1.0
0.0
0.0
1.0
0.0
16:
7.0
0.0
0.0
1.0
0.0
1.0
1.0
1.0
1.0
1.0
0.0
1.0
0.0
17:
3.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
18:
6.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
1.0
1.0
0.0
1.0
1.0
19:
5.0
1.0
1.0
1.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
Module DISPLY
266
WDM
121
Temp
erat
ure
(Deg
. F)
Summ
ary
for
MONT
H 19
76/8
/
Data
int
erva
l:
120
mins
DAY
AVER
Inte
rval
Num
ber
12
34
56
78
910
1112
167
.758
.061
.355
.161
.369
.075
.679
.881
.877
.471
.465
.961
.22
67.2
57.5
55.2
54.2
60.8
69.1
76.1
80.7
82.8
77.6
70.4
64.0
58.4
366
.754
.151
.550
.257
.867
.275
.180
.482
.878
.672
.867
.562
.84
72.8
59.3
57.2
56.2
63.5
72.7
80.3
85.4
87.8
84.4
79.6
75.4
71.6
573
.068
.867
.066
.169
.874
.378
.280
.781
.978
.674
.169
.966
.36
68.3
63.6
61.9
61.1
65.4
70.8
75.4
78.4
79.9
74.9
68.2
62.2
56.9
765
.252
.850
.449
.257
.066
.774
.880
.382
.877
.470
.163
.557
.78
66.0
53.2
50.5
49.2
57.2
67.1
75.6
81.1
83.8
78.6
71.4
65.0
59.4
971
.455
.152
.551
.259
.970
.879
.985
.988
.885
.280
.275
.871
.910
76.5
68.9
67.1
66.1
71.2
77.4
82.7
86.2
87.9
84.4
79.6
75.4
71.6
1175
.968
.867
.066
.170
.976
.981
.985
.386
.983
.478
.674
.470
.612
75.5
67.8
66.0
65.1
70.4
76.9
82.4
86.2
87.9
83.7
78.1
73.1
68.6
1370
.765
.263
.262
.266
.271
.375
.778
.679
.977
.073
.269
.766
.614
68.6
64.2
62.8
62.1
66.0
70.8
74.9
77.6
78.9
74.6
68.8
63.5
58.8
1563
.455
.353
.252
.257
.964
.971
.074
.976
.972
.265
.960
.355
.416
63.3
51.6
49.3
48.2
54.8
63.0
70.1
74.7
76.8
73.7
69.4
65.5
62.1
1771
.559
.557
.957
.163
.872
.179
.183
.785
.882
.76
.972
.268
.218
73.0
65.0
63.1
62.1
67.4
73.9
79.4
83.2
84.9
81.2
76.2
71.8
67.9
1972
.864
.963
.162
.167
.674
.580
.284
.185
.981
.475
.469
.965
.120
72.6
61.5
59.2
58.2
65.1
73.6
80.8
85.6
87.8
83.4
77.4
71.9
67.1
2175
.963
.561
.260
.267
.576
.784
.389
.491
.887
.481
.475
.971
.122
76.2
67.5
65.2
64.2
69.9
76.9
83.0
86.9
88.9
85.1
79.9
75.2
71.2
2374
.968
.066
.165
.170
.276
.481
.785
.286
.982
.777
.172
.167
.624
73.0
64.2
62.2
61.2
66.6
73.5
79.2
83.1
84.9
81.6
77.1
72.9
69.3
2575
.666
.664
.964
.169
.976
.983
.086
.988
.984
.678
.873
.568
.926
78.5
65.3
63.2
62.2
69.5
78.7
86.3
91.4
93.8
90.1
84.9
80.2
76.2
2773
.473
.071
.170
.172
.876
.279
.181
.181
.977
.471
.465
.961
.2
Module DISPLY
267
WDM
121
Temp
erat
ure
(Deg
. F)
Annu
al d
ata
disp
lay:
Sum
mary
for
per
iod
endi
ng 1
976/
12
DAY
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
127
.69.
433
.149
.249
.467
.567
.067
.768
.368
.245
.711
.82
18.4
17.7
33.3
48.5
45.3
66.3
67.3
67.2
68.9
66.8
42.7
9.1
37.
021
.929
.849
.349
.765
.170
.566
.772
.765
.935
.313
.44
13.8
12.6
28.6
48.9
59.7
68.7
73.0
72.8
67.8
58.5
33.0
16.5
520
.315
.925
.052
.057
.371
.072
.773
.066
.146
.536
.718
.06
18.0
24.8
25.7
50.8
49.0
71.4
72.8
68.2
71.8
42.4
32.9
4.2
70.
532
.531
.147
.850
.673
.176
.165
.271
.843
.429
.12.
58
5.5
36.7
32.7
46.1
55.2
74.0
77.4
66.0
65.4
45.5
34.1
17.7
99.
939
.035
.153
.664
.272
.682
.071
.463
.251
.535
.523
.110
16.6
36.5
38.8
54.1
60.9
75.2
84.9
76.5
62.3
56.0
25.2
9.3
1130
.444
.236
.443
.658
.877
.281
.975
.966
.362
.620
.311
.312
26.7
46.0
32.2
48.7
57.0
78.8
77.7
75.5
68.6
59.9
20.7
11.7
1327
.537
.134
.054
.858
.572
.280
.970
.769
.059
.523
.920
.414
30.1
41.2
33.2
64.7
63.0
72.2
81.3
68.6
68.0
57.2
27.5
29.7
1528
.544
.027
.567
.660
.169
.673
.863
.459
.143
.532
.828
.416
13.5
34.6
30.7
71.1
58.2
68.2
68.1
63.3
60.4
39.4
36.7
35.2
1711
.633
.644
.165
.756
.269
.869
.971
.566
.934
.846
.038
.918
21.5
39.6
55.7
59.2
58.3
65.5
72.7
73.0
62.2
32.9
40.8
36.5
1920
.739
.655
.848
.468
.365
.878
.972
.862
.540
.135
.527
.720
18.8
34.2
48.1
48.0
71.5
66.7
76.0
72.6
58.9
37.4
29.7
9.7
2125
.527
.735
.754
.965
.367
.171
.875
.961
.937
.827
.617
.122
25.7
29.0
43.8
50.8
58.8
69.6
78.9
76.2
58.2
35.5
23.1
17.1
2328
.636
.355
.250
.659
.266
.579
.874
.954
.438
.828
.822
.324
27.2
40.8
55.8
47.0
58.1
71.1
74.3
73.0
51.0
42.1
41.0
23.8
2522
.647
.549
.343
.460
.172
.571
.975
.654
.336
.941
.725
.126
10.1
44.8
45.5
43.8
62.0
73.9
75.4
78.5
56.1
37.1
28.7
30.5
2718
.141
.247
.346
.365
.974
.173
.873
.453
.737
.310
.322
.328
22.2
39.2
45.3
48.5
62.5
72.2
73.9
65.8
57.6
40.2
6.3
3.8
Module DURANL
268
4.2(14) Perform Duration Analysis on a Time Series (Utility Module DURANL)
This module examines the behavior of a time series, computing a variety ofstatistics relating to its excursions above and below certain specified levels(Figure 4.2(14)-1). Sample printout is shown in Figure 4.2(14)-2. The quantity ofprintout produced can be regulated by the user with a print-level-flag (PRFG),which has a valid range of values from 1 through 6.
The basic principles are:
1. The module works on the time series after it has been placed in the INPAD.The data are, thus, at the internal time step of the operation (INDELT). Thismodule operates on a mean-valued input time series. Therefore, if a point-valued time series is routed to it, TSGET will, by default, generate meanvalues for each time step, and these will be analyzed.
2. When the value of the time series rises above the user-specified level, apositive excursion commences. When it next falls below the level, thisexcursion ends. A negative excursion is defined in the reverse way. (Figure4.2(14)-1).
3. If the time series has a value less than -10.0**10 this is considered to bean undefined event (e.g., the concentration of a constituent when there is nowater). In this case the value is in a special category - it is in neithera positive nor a negative excursion.
4. The above is true if the specified duration is one time step. In this case,the results produced include a conventional frequency analysis (e.g., flowduration) of the data. However, the user may specify up to 10 durations; eachis given as a multiple (N) of the basic time step (INDELT), Then, for anexcursion or undefined event to be considered, it has to endure for at leastN consecutive intervals; else it is ignored.
5. The user may specify an analysis season. This is a period (the same in eachyear) for which the data will be analyzed (e.g., Oct 1 through May 10). Datafalling outside the analysis season will not be considered.
Module DURANL
269
Value | Time(of time | .---. <====Time Series --------> series or| | |"level") | .--' | .------. | | | | .---. | | | | .--' | | | | '---. | | | 3+ '--. | '---' 3+ | 3rd V +10|-----|------------|-----------------------|------------------|-------- | | | 3- | | level | .--' | | | | | | | '---. 0| | | | | |--' | .---. | | | | | | | '---- | | .--' | .---' | 2+ '---.2-| 2+ | 2- | 2+ -10|----------------------|--|------|-----|------------------------------- | | | | | 2nd level | 1+ '--' | | 1+ -20|--------------------------------|-----|------------------------------- | |1-.--' 1st level | | | | '--' | Legend: 2+ excursion above second level (duration >=1) 2- excursion below second level (duration >=1) etc.
Figure 4.2(14)-1 Definition of terms used in duration analysis module
Module DURANL
270
The analyses performed, and printout produced (Figure 4.2(14)-2), are:
1. Introductory information - Title, start and end date/time, analysis season.
2. The next seven sets of tables are all similar in format; each contains dataon positive and negative excursions, for each level and duration, andinformation on undefined event conditions which persisted for each of thespecified durations. The value of PRFG required to generate each of these,and the table heading and the data displayed in it are:
a) PRFG > 0. "Fraction of time spent in excursions at each level withduration greater than or equal the specified durations. The fraction isrelative to the total time span." These are the fractions of totalconsidered time that each of the above-defined conditions existed.
b) PRFG > 1. "Fraction of time spent in excursions at each level withduration greater than or equal the specified durations. The fraction isrelative to the time spent in excursions at each level." In the"Positive Excursions" table, this gives, for each specified level, thetotal time that an excursion of duration N existed, divided by the totaltime that an excursion of duration 1 existed. A similar definition holdsfor the numbers in the "Negative Excursions" table.
c) PRFG > 2. "Time spent in excursions at each level with duration greaterthan or equal the specified durations." The tables give the total numberof time steps for which the various conditions occurred.
d) PRFG > 3. "Number of excursions at each level with duration greater thanor equal the specified durations". These give the total number of eventsthat were found (number of positive and negative excursions for eachlevel and duration, and number of "undefined occurrences" of eachduration).
e) PRFG > 4. "Average duration of excursions at each level given that theduration is greater than or equal the specified durations". These valuesanswer the question: "given that a specified excursion or undefined con-dition occurred, what was the mean number of time steps that itpersisted?"
f) PRFG > 5. "Standard deviation of duration of excursions at each levelgiven that the duration is greater than or equal the specifieddurations." These tables are similar to those discussed in (e) above,except that the standard deviation, instead of the mean, is considered.
g) PRFG > 6. "Fraction of excursions with duration N with respect to thetotal number of excursions (duration 1) for each level". These tablesgive the number of excursions at each duration divided by the number ofexcursions at duration 1 for each level.
3. Summary information: Total number of time intervals analyzed, total numberof time intervals for which values were "undefined", total number of daysanalyzed, sample size, max, min, mean, standard deviation.
Module DURANL
271
Duration analysis operation no. 1 Analysis of Subb. 4 Outflow (cfs) Start date: 1972/12/31 24: 0 End date: 1974/12/31 24: 0Analysis season starts: 2/28 24: 0 Ends: 11/30 24: 0
PERCENT OF TIME TABLES (WITH RESPECT TO THE TOTAL SPAN OF TIME)
POSITIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 1.000 1.000 1.000 10.00 .7308 .7259 .7235 20.00 .5128 .5062 .5034 50.00 .1790 .1674 .1633 500.0 .2273E-02 .0000E+00 .0000E+00
NEGATIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 .0000E+00 .0000E+00 .0000E+00 10.00 .2692 .2655 .2645 20.00 .4872 .4813 .4762 50.00 .8210 .8121 .8030 500.0 .9977 .9977 .9977
UNDEFINED EVENTS (NO WATER) DURATIONS 1 12 24 .0000E+00 .0000E+00 .0000E+00
Figure 4.2(14)-2 Sample Duration Analysis Printout [Continued on next 2 pages]
Module DURANL
272
PERCENT OF TIME TABLES (WITH RESPECT TO THE TIME SPENT IN EXCURSIONS)
POSITIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 1.000 1.000 1.000 10.00 1.000 .9933 .9899 20.00 1.000 .9871 .9817 50.00 1.000 .9353 .9124 500.0 1.000 .0000E+00 .0000E+00
NEGATIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 .0000E+00 .0000E+00 .0000E+00 10.00 1.000 .9862 .9828 20.00 1.000 .9879 .9775 50.00 1.000 .9892 .9780 500.0 1.000 1.000 1.000 UNDEFINED EVENTS (NO WATER) DURATIONS 1 12 24 .0000E+00 .0000E+00 .0000E+00 TIME SPENT IN EXCURSIONS
POSITIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 .1320E+05 .1320E+05 .1320E+05 10.00 9647. 9582. 9550. 20.00 6769. 6682. 6645. 50.00 2363. 2210. 2156. 500.0 30.00 .0000E+00 .0000E+00 NEGATIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 .0000E+00 .0000E+00 .0000E+00 10.00 3553. 3504. 3492. 20.00 6431. 6353. 6286. 50.00 .1084E+05 .1072E+05 .1060E+05 500.0 .1317E+05 .1317E+05 .1317E+05UNDEFINED EVENTS (NO WATER) DURATIONS 1 12 24 .0000E+00 .0000E+00 .0000E+00
Module DURANL
273
STANDARD DEVIATION OF TIME SPENT IN EXCURSIONS
POSITIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 .0000E+00 .0000E+00 .0000E+00 10.00 922.9 2032. 2181. 20.00 321.6 581.1 602.0 50.00 71.65 132.1 128.7 500.0 .7423 .0000E+00 .0000E+00
NEGATIVE EXCURSIONS DURATIONS 1 12 24LEVELS .0000E+00 .0000E+00 .0000E+00 .0000E+00 10.00 107.2 113.8 113.3 20.00 127.0 140.0 141.4 50.00 167.6 188.1 191.6 500.0 1202. 1202. 1202.
UNDEFINED EVENTS (NO WATER) DURATIONS 1 12 24 .0000E+00 .0000E+00 .0000E+00
SUMMARY TOTAL LENGTH OF DEFINED EVENTS: 13200. INTERVALS TOTAL LENGTH OF UNDEFINED EVENTS: 0. INTERVALS TOTAL LENGTH OF ANALYSIS: 550. DAYS SAMPLE SIZE: 13200 SAMPLE MAXIMUM: .1307E+05 SAMPLE MINIMUM: 2.290 SAMPLE MEAN: 37.80 SAMPLE STANDARD DEVIATION: 164.0
Module DURANL
274
4. Lethality analysis:
The function of this section of the DURANL module is to assess the risk associatedwith any contaminant concentration time series generated by the HSPF applicationmodules. The methodology links frequency data on instream contaminant levels totoxicity information resulting from both acute and chronic laboratory bioassays.The methodology is based on the Frequency Analysis of Concentration (FRANCO)program developed by Battelle, Pacific Northwest Laboratories as part of theirChemical Migration and Risk Assessment (CRMA) Methodology.
Laboratory toxicity experiments provide the main basis for developing a riskanalysis for fish or other aquatic organisms. A common method of summarizing theresults of these experiments is to use a lethal concentration where 50% of the fishdie (LC50). Usually information for LC50 concentrations at 24, 48, and 96 hourscan be derived from laboratory experiments in the form of pairs of lethalconcentration and duration values. By connecting these pairs with straight linesegments and extending the function in a reasonable manner at each end, a functionis defined such that an event defined by a particular concentration level with aparticular duration can be classified as exceeding or not exceeding the function,i.e., exceeding an LC50 value. (Figure 4.2(14)-3). An event exceeds the LCfunction when the concentration defining the event and the duration of the eventresults in the pair falling above and to the right of the combined LC50, or globalexceedance, curve.
If LCNUM is greater than zero, a global exceedance summary table is printed whichgives the fraction of time that a global exceedance curve is exceeded. Up to 5 LCcurves can be analyzed at one time. It should be noted that the global exceedancesummary eliminates double counting by reporting only those exceedance events withthe lowest concentrations that occur in different contaminant peaks. (FRANCOdocumentation should be consulted for more detailed discussion).
If LCOUT=1 and LCNUM=0, a lethal event summary is printed to supplement the globalexceedance information. The table gives a summary of all lethal events includingending time, lethal curve number, number of intervals in event, and concentrationlevel. Printout is to unit PUNIT, which should be unique to the duration analysis;otherwise, the output from the lethal event summary will be mixed with the printoutfrom application modules.
Module DURANL
275
Figure 4.2(14)-3 Sample Lethal Concentration (LC) Function for Global Exceedance Calculation
Module GENER
276
4.2(15) Generate a Time Series from One or Two Other Time Series (Utility Module GENER)
This module is designed to perform any one of several possible transformations oninput time series. The transformation is specified by supplying an "option code"(OPCODE). If A and B are the input time series and C is the computed time series,then the transformations performed for each possible value of OPCODE are:
OPCODE Action
1 C= Abs value (A) 2 C= Square root (A) 3 C= Truncation (A) e.g. If A=4.2, C=4.0 A=-3.5, C=-3.0 4 C= Ceiling (A). The "ceiling" is the integer >= given value. e.g. If A=3.5, C=4.0 A=-2.0, C=-2.0 5 C= Floor (A). The "floor" is the integer <= given value. e.g. If A=3.0, C=3.0 A=-2.7, C=-3.0 6 C= loge (A) 7 C= log10 (A) 8 C= K(1)+K(2)*A+K(3)*A**2 (up to 7 terms) The user supplies the number of terms and the values of the coefficients (K). 9 C= K**A 10 C= A**K 11 C= A+K 12 C= Sin (A) 13 C= Cos (A) 14 C= Tan (A) 15 C= Sum (A) 16 C= A+B 17 C= A-B 18 C= A*B 19 C= A/B 20 C= MAX (A,B) 21 C= MIN (A,B) 22 C= A**B 23 C= cumulative departure of A below B 24 C= K 25 C= Max (A,K) 26 C= Min (A,K)
Module GENER
277
Note that if OPCODE is less than 15, or OPCODE equals 25 or 26, only one input timeseries is involved (unary operators); if OPCODE is 24, no inputs are required(constant); otherwise two inputs are required (binary operators). As with theother operating modules, the input time series are first placed in the INPAD bymodule TSGET (This may involve a change of time step and/or "kind"). Therefore,by the time module GENER works on them, they are mean valued time series with atime step equal to INDELT.
Module MUTSIN
278
4.2(16) Multiple Sequential Input of Time Series from an HSPF Stand Alone Plotter File (Utility Module MUTSIN)
This utility module reads a sequential external file previously written on disk.This file has the same format as the PLOTFL produced with utility module PLTGEN(Section 4.2(12)). The user specifies the number of point and/or mean-valued timeseries to be read and the number of lines to skip at the beginning of the externalfile.
The missing data flag, MISSFG, is used to specify how MUTSIN reacts to missingdata. A MISSFG value of 0 indicates that MUTSIN is to report an error and quit ifany data are missing. Therefore, in this case, the internal time-step (DELT) mustequal the time-step of the external file, the starting time of the run mustcorrespond with the first entry read from the external file, and no entries may bemissing. A MISSFG value of 1 indicates that MUTSIN is to fill missing sequentialfile entries with 0.0. A MISSFG value of 2 indicates that MUTSIN is to fillmissing entries with -1.0E30. A MISSFG value of 3 indicates that MUTSIN is to fillmissing values with the value of the next available entry.
Note that the date and time appearing in each record of the file must be in thesame format as that used by the PLTGEN module to write a PLOTFL. (Section4.2(12)). That is, the full year/month/day/hour/minute string must be present anda time, e.g., midnight is coded as 74 01 02 24 00, not 75 01 03 00 00.
The EXT TARGETS and/or NETWORK blocks are used to specify where TSPUT places thetime series data read in from the external file.
MUTSIN has four potential uses:
1. It may be used to form a simple interface with other continuous models. Theother model can output its results in the form of an HSPF PLOTFL (or a formatconversion program can be used), and MUTSIN can be used to input this data toHSPF. Conversely, data can be output from HSPF, using the PLTGEN module, forinput to the other model.
2. MUTSIN may be used to transfer data in a WDM file to another WDM file. Thistransfer requires the use of PLTGEN to output the data from the source fileand MUTSIN to input to the target file.
3. MUTSIN can be used to transfer data between different types of computerhardware where WDM or DSS files are incompatible (e.g., Unix to personalcomputer and vice versa).
4. MUTSIN may also be used to input point valued data or data with a timeinterval not included in the standard HSPF sequential input formats (Part F,Section 4.9).
Module TSPUT
279
4.3 Module TSPUT
Module TSPUT is complementary to, and may be viewed as a mirror image of, moduleTSGET (Section 4.1). TSGET obtains time series from a WDM file, DSS, sequentialfile, or the INPAD and places its output in the INPAD. Conversely, TSPUT obtainsa time series from the INPAD and places its output in the WDM file, DSS, or backin the INPAD. It has similar capabilities to TSGET, to alter the time step, "kind"or to perform a linear transformation on the time series with which it deals.
Compared to TSGET, module TSPUT contains one major complicating factor. When atime series is to be written to a WDM or DSS data set, the action taken depends onhow any pre-existing data are to be treated. The possible access modes, ADD andREPL, are discussed in Part F, Section 4.6.
REFERENCES
280
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the Hydrological Simulation Program - Fortran (HSPF). Krone, R.B. 1962. Flume Studies of the Transport of Sediment in Estuarial
Shoaling Processes. Hydraulic Engineering Laboratory and Sanitary EngineeringResearch Laboratory, University of California, Berkeley, CA.
Loehr, R.C., T.B.S. Prakasam, E.G. Srinath, and Y.D. Joo. 1973. Development and
Demonstration of Nutrient Removal from Animal Wastes, U.S. EnvironmentalProtection Agency, Washington, D.C. EPA R2-73-095.
Mabey, W.R., T. Mill, and D.G. Hendry. 1980. Photolysis in Water, Chapter 3 inMill et al., 1980.
Meyer, L.D., and W.H. Wischmeier. 1969. Mathematical Simulation of the Process
of Soil Erosion by Water. Trans. Am. Soc. Agric. Eng. 12(6):754-758,762.
Mill, T., W.R. Mabey, D.C. Bomberger, T.-W. Chou, D.G. Hendry, and J.H. Smith. 1980. Laboratory Protocols for Evaluating the Fate of Organic Chemicals inAir and Water. SRI International, Menlo Park, CA, Environmental ResearchLaboratory, Athens, GA.
Mill, T., W.R. Mabey, and D.G. Hendry. 1980. Hydrolysis in Water, Chapter 2 inMill et al., 1980.
REFERENCES
282
Mill, T., W.R. Mabey, and D.G. Hendry. 1980. Oxidation in Water, Chapter 4 inMill et al., 1980.
Negev, M. 1967. A Sediment Model on a Digital Computer, Department of CivilEngineering, Stanford University, Stanford, CA, Technical Report No. 76, 109p.
O'Connor, D.J., and D.M. Di Toro. 1970. Photosynthesis and Oxygen Balance inStreams. ASCE, Journal of Sanitation Engineering Div., 96(SA2):7240.
O'Connor, D.J., and W.E. Dobbins. 1958. Mechanism of Reaeration in NaturalStreams. Trans. of the ASCE, Vol. 123, p. 641-684.
Onishi, Y. and S.E. Wise. 1979. Mathematical Model, SERATRA, forSediment-Contaminant Transport in Rivers and its Application to PesticideTransport in Four Mile and Wolf Creeks in Iowa. Battelle, Pacific NorthwestLaboratories, Richland, WA.
Onstad, C.A., and G.R. Foster. 1975. Erosion Modeling on a Watershed, Trans. of
ASAE, 18(2):288-292. Owens, M., R.W. Edwards, and J.W. Gibbs. 1964. Some Reaearation Studies in
Streams, Intl. Journal of Air and Water Pollution, Vol. 8, p. 469-486. Partheniades, E. 1962. A Study of Erosion and Deposition of Cohesive Soils in
Salt Water, Ph.D. Thesis, University of California, Berkeley, CA.
Philips, J.R. 1957. The Theory of Infiltration: The Infiltration Equation andIts Solution, Soil Science, 83:345-375.
Reddy, K.R, R. Khaleel, M.R. Overcash, and P.W. Westerman. 1979. A Nonpoint SourceModel for Land Areas Receiving Animal Wastes: II. Ammonia Volatilization,Trans. of ASAE, 22(6):1398-1405.
Richman, S. 1958. The Transformation of Energy by Daphnia pulex. Ecolog. Monogr.Vol. 28, p. 273-291.
Schindler, D.W. 1968. Feeding, Assimilation and Respiration Rates of Daphnia
magna Under Various Environmental Conditions and their Relation to ProductionEstimates, Journal of Animal Ecology, Vol. 37, p. 369-385.
Shaffer, M.J., A.D. Halvorson, and F.J. Pierce. 1991. Nitrate Leaching and EconomicAnalysis Package (NLEAP): Model Description and Application. In: ManagingNitrogen for Groundwater Quality and Farm Profitability. R.F. Follett, D.R.Keeney, and R.M. Cruse (eds.). Soil Science Society of America, Inc.
Smith, J.H., and D.C. Bomberger. 1980. Volatilization from Water, Chapter 7 inMill et al., 1980.
Smith, J.H., W.R. Mabey, N. Bohonos, B.R. Holt, S.S. Lee, T.-W. Chou, D.C.
Bomberger, and T. Mill. 1977. Environmental Pathways of Selected Chemicalsin Freshwater Systems, Part I: Background and Experimental Procedures.Environmental Research Laboratory, Athens, GA, EPA 600/7-77-113.
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283
Thomann, R.V. 1972. Systems Analysis and Water Quality Management. McGraw-Hill,
Inc., New York, 286p.
Tsivoglou, E.C., and J.R. Wallace. 1972. Characterization of Stream ReaerationCapacity, U.S. Environmental Protection Agency, EPA R3-72-012.
U.S. Army Corps of Engineers. 1956. Snow Hydrology, Summary Report of the SnowInvestigations, North Pacific Division. Portland, OR, 437p.
U.S. Environmental Protection Agency. 1975. Process Design Manual for Nitrogen
Control, Office of Technology Transfer, Washington D.C.
Vanomi, V.A., Editor. 1975. Sedimentation Engineering. Prepared by the ASCE TaskCommittee for the Preparation of the Manual on Sedimentation of theSedimentation Committee of the Hydraulics Division, New York.
Wezerak, C.G., and J.J. Gannon. 1968. Evaluation of Nitrification in Streams. ASCE, Journal of the Sanitation Engineering Div., 94(SA5):6159.
Wischmeier, W.H., and D.D. Smith. 1965. Predicting Rainfall Erosion Losses fromCropland East of the Rocky Mountains. U.S. Department of Agriculture,Agricultural Handbook No. 282. 47p.
Zepp, R.G. and D.M. Cline. 1977. Rates of Direct Photolysis in Aquatic
Environments, Environmental Science and Technology, 11:359-366.
User's Control Input
284
PART F
FORMAT FOR THE USER'S CONTROL INPUT
CONTENTS
1.0 General Information and Conventions . . . . . . . . . . . . . . . 286 1.1 The Users Control Input. . . . . . . . . . . . . . . . . . . 286 1.2 General Comments on Method of Documentation . . . . . . . . 286
2.0 Format of a TSSMGR Input Set (omitted). . . . . . . . . . . . . 287
3.0 Sample TSSMGR Input Set (omitted) . . . . . . . . . . . . . . . 287
4.0 Format of the Users Control Input . . . . . . . . . . . . . . . . 288 4.1 GLOBAL Block . . . . . . . . . . . . . . . . . . . . . . . . 290 4.2 FILES Block . . . . . . . . . . . . . . . . . . . . . . . . 292 4.3 OPN SEQUENCE Block . . . . . . . . . . . . . . . . . . . . . 295 4.3.1 Optimization of Operation Sequencing . . . . . . . . . 296 4.4 <Operation-type> Block . . . . . . . . . . . . . . . . . . . 298 4.4(1) PERLND Block . . . . . . . . . . . . . . . . . . . . 300 4.4(1).1 General input . . . . . . . . . . . . . . 301 4.4(1).2 Section ATEMP input . . . . . . . . . . . 307 4.4(1).3 Section SNOW input . . . . . . . . . . . . 309 4.4(1).4 Section PWATER input . . . . . . . . . . . 317 4.4(1).5 Section SEDMNT input . . . . . . . . . . . 334 4.4(1).6 Section PSTEMP input . . . . . . . . . . . 342 4.4(1).7 Section PWTGAS input . . . . . . . . . . . 354 4.4(1).8 Section PQUAL input . . . . . . . . . . . 363 4.4(1).9 Section MSTLAY input . . . . . . . . . . . 376 4.4(1).10 Section PEST input . . . . . . . . . . . . 385 4.4(1).11 Section NITR input . . . . . . . . . . . . 399 4.4(1).12 Section PHOS input . . . . . . . . . . . . 436 4.4(1).13 Section TRACER input . . . . . . . . . . . 452 4.4(2) IMPLND Block . . . . . . . . . . . . . . . . . . . . 457 4.4(2).1 General input . . . . . . . . . . . . . . 457 4.4(2).2 Section ATEMP input . . . . . . . . . . . 463 4.4(2).3 Section SNOW input . . . . . . . . . . . . 463 4.4(2).4 Section IWATER input . . . . . . . . . . . 463 4.4(2).5 Section SOLIDS input . . . . . . . . . . . 471 4.4(2).6 Section IWTGAS input . . . . . . . . . . . 477 4.4(2).7 Section IQUAL input . . . . . . . . . . . 483
User's Control Input
285
4.4(3) RCHRES Block . . . . . . . . . . . . . . . . . . . . 489 4.4(3).1 General input . . . . . . . . . . . . . . 489 4.4(3).2 Section HYDR input . . . . . . . . . . . . 495 4.4(3).3 Section ADCALC input . . . . . . . . . . . 513 4.4(3).4 Section CONS input . . . . . . . . . . . . 515 4.4(3).5 Section HTRCH input . . . . . . . . . . . 520 4.4(3).6 Section SEDTRN input . . . . . . . . . . . 528 4.4(3).7 Section GQUAL input . . . . . . . . . . . 537 4.4(3).8 Input for RQUAL sections . . . . . . . . . 572 4.4(3).8.1 Section OXRX input . . . . . . 575 4.4(3).8.2 Section NUTRX input . . . . . 586 4.4(3).8.3 Section PLANK input . . . . . 597 4.4(3).8.4 Section PHCARB input . . . . . 609 4.4(11) COPY Block . . . . . . . . . . . . . . . . . . . . . 613 4.4(12) PLTGEN Block . . . . . . . . . . . . . . . . . . . . 615 4.4(13) DISPLY Block . . . . . . . . . . . . . . . . . . . . 621 4.4(14) DURANL Block . . . . . . . . . . . . . . . . . . . . 626 4.4(15) GENER Block. . . . . . . . . . . . . . . . . . . . . 634 4.4(16) MUTSIN Block . . . . . . . . . . . . . . . . . . . . 640 4.5 FTABLES Block. . . . . . . . . . . . . . . . . . . . . . . . 642 4.6 Time Series Linkages . . . . . . . . . . . . . . . . . . . . 645 4.7 Time Series Catalog . . . . . . . . . . . . . . . . . . . . 668 4.7.1 Connection of Surface and Instream Modules . . . . . 669 4.7.2 Atmospheric Deposition of Water Quality Constituents . . . . . . . . . . . . . . . . . . . . 669 4.7(1) Catalog for PERLND Module . . . . . . . . . . . . . 671 4.7(2) Catalog for IMPLND Module . . . . . . . . . . . . . 692 4.7(3) Catalog for RCHRES Module . . . . . . . . . . . . . 698 4.7(11) Catalog for COPY Module . . . . . . . . . . . . . . 721 4.7(12) Catalog for PLTGEN Module . . . . . . . . . . . . . 722 4.7(13) Catalog for the DISPLY Module. . . . . . . . . . . . 723 4.7(14) Catalog for the DURANL Module. . . . . . . . . . . . 724 4.7(15) Catalog for the GENER Module . . . . . . . . . . . . 725 4.7(16) Catalog for the MUTSIN Module. . . . . . . . . . . . 726 4.8 FORMATS Block. . . . . . . . . . . . . . . . . . . . . . . . 727 4.9 Sequential and PLTGEN/MUTSIN File Formats . . . . . . . . . 728 4.10 SPEC-ACTIONS Block . . . . . . . . . . . . . . . . . . . . . 731 4.11 MONTH-DATA Block . . . . . . . . . . . . . . . . . . . . . . 742 4.12 CATEGORY Block . . . . . . . . . . . . . . . . . . . . . . . 743
FIGURES
Number Page
4.7(1)-1 Groups of time series associated with the PERLND module . . 6734.7(2)-1 Groups of time series associated with the IMPLND module . . 6934.7(3)-1 Groups of time series associated with the RCHRES module . . 700
User's Control Input
286
1.0 GENERAL INFORMATION AND CONVENTIONS
1.1 The User's Control Input
The User's Control Input (UCI) consists of text lines limited to 80 characters. A general feature of the UCI is that the lines are collected into groups. Groups may contain subordinate groups; that is, they may be nested. In everycase, a group commences with a heading (such as RUN) and ends with a delimiter(such as END RUN).
The HSPF system will ignore any line in the UCI which contains three or moreconsecutive asterisks (***), just as a computer language compiler bypassescomments in a source program. Blank lines are also ignored. This feature canbe used to insert headings and comments which make the text more intelligible tothe reader, but are not required or read by the HSPF system itself.
The body of the User's Control Input consists of one or more major groups oftext, called RUN input sets:
<RUN input set 1> <RUN input set 2> ----- -----
A RUN input set contains all the input needed to perform a single RUN. A RUN isa set of operations with a common START date-time and END date-time.
1.2 General Comments on Method of Documentation
The documentation of each portion of the UCI is divided into three sections:"layout", "details", and "explanation".
The "layout" section shows how the input is arranged. Text always appearing inthe same form (e.g., RUN) is shown in upper case. Text which varies from job tojob is shown by lower case symbols enclosed in angle brackets (<spa>). Linescontaining illustrative text, not actually required by the system, have threeconsecutive asterisks, just as they might have in the UCI. Optional material,or that which is not always required, is enclosed in brackets []. The columnnumbers printed at the head of each layout show the starting location of eachkeyword and symbol.
The "details" section describes the input values required for each symbolappearing in the layout. The Fortran identifiers used to store the value(s) inthe code are given, followed by the format. The field(s) specified in thisformat start in the column containing the < which immediately precedes thesymbol in the layout. For example, < range >, which consists of the startingand ending operations that the current line applies to, starts in column 1 andends in column 10. Where relevant, the Details section also indicates defaultvalues and minimum and maximum values for each item in the UCI.
User's Control Input
287
The "explanation" section contains any necessary explanatory material whichcould not fit into the details section.
User's Control Input
288
2.0 FORMAT OF A TSSMGR DATA SET (omitted)
Note: the TSSMGR module and all other TSS functionality is no longer documentedor maintained; refer to Version 10 (or earlier) documentation for details.
3.0 SAMPLE TSSMGR INPUT SET (omitted)
RUN Input Set
289
4.0 FORMAT OF THE USERS CONTROL INPUT
Summary
The User's Control Input starts with a RUN heading and ends with an END RUNdelimiter. The body of the text consists of several groups, called "blocks,"which may appear in any sequence:
RUN
GLOBAL Block
Contains information of a global nature. It applies to every operation in the RUN.
FILES Block
Specifies disk files to be used by the run and their file unitnumbers.
OPN SEQUENCE Block
Specifies the operations to be performed in the RUN, in the sequence they will be executed. It indicates any grouping (INGROUPs).
<Operation-type> Block
Deals with data pertaining to all the operations of the same<Operation-type>, for example, parameters and initial conditions forall Pervious Land-segments in a RUN. It is not concerned withrelationships between operations, or with external sources or targetsfor time series. There is one <Operation-type> Block for each<operation-type> involved in the RUN.
[FTABLES Block]
A collection of function tables (FTABLES). A function table is usedto document, in discrete numerical form, a functional relationshipbetween two or more variables. FTABLES are used to specify the depth-volume-discharge relationship for RCHRES operations.
[EXT SOURCES Block]
Specifies time series which are input to the operations from externalsources (WDM file, DSS file, or sequential (SEQ) files).
[NETWORK Block]
Specifies any time series which are passed between operations.
RUN Input Set
290
[EXT TARGETS Block]
Specifies those time series which are output from operations toexternal destinations (WDM or DSS file).
[SCHEMATIC Block]
Specifies structure of watershed, i.e., connections of land segmentsand stream reaches to each other. Operates in tandem with MASS-LINKblock to simplify definition of complex watersheds.
[MASS-LINK Block]
Specifies groups of time series to combine with network connectionsdefined in the SCHEMATIC block in order to specify mass flows in thewatershed.
[MONTH-DATA Block]
Specifies monthly values of atmospheric deposition fluxes andconcentrations (in rain) for water quality constituents.
[CATEGORIES Block]
Specifies the number of water categories to be simulated in thestreams and reservoirs represented by RCHRES operations.
[PATHNAMES Block]
Associates DSS pathnames with data-set ID numbers for all DSS datasets in the EXT SOURCES and EXT TARGETS blocks.
[FORMATS Block]
Contains any user-supplied formats which may be required to read timeseries on external sequential (SEQ) files.
[SPEC-ACTIONS Block]
Specifies operation, variable location, type or name, date/time andaction code in order to change a variable's value during a run.
END RUN
Usually, a User's Control Input will not include all of the above blocks. Theirpresence will be dictated by the operations performed in the RUN and the optionswhich are selected.
GLOBAL Block
291
4.1 GLOBAL Block
This block must always be present in a RUN input set.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******GLOBAL <------------------------------- run-info ----------------------------------> START <---s-date-time----> END<---e-date-time----> RUN INTERP OUTPT LEVELS<lev><spa> RESUME <res> RUN <run> UNITS <ufg> END GLOBAL ********************************************************************************Example*******
GLOBAL Seven Mile River - Water quality run START 1980/01/01 00:00 END 1987/12/31 12:00 RUN INTERP OUTPT LEVELS 4 3 RESUME 0 RUN 1 UNITS 1END GLOBAL********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<run-info> RUNINF(20) A78 none none none<s-date-time> SYR, I8, none 1 32767 SMO, 1X,I2, 1 1 12 SDA, 1X,I2, 1 1 varies SHR, 1X,I2, 0 0 23 SMI 1X,I2 0 0 59<e-date-time> EYR, I8, none 1 32767 EMO, 1X,I2, 12 1 12 EDA, 1X,I2, varies 1 varies EHR, 1X,I2, #24 0 24 #only if EMI is 0 EMI 1X,I2 0 0 59<lev> OUTLEV I5 0 0 10<spa> SPOUT I5 2 0 10<res> RESMFG I5 0 0 1<run> RUNFG I5 0 0 1<ufg> EMFG I5 1 1 2--------------------------------------------------------------------------------
GLOBAL Block
292
Explanation
RUNINF stores the users title/comments regarding the RUN.
Users conventionally label the same point in time differently, depending whetherthey are looking forward or backward towards it. For example, if we say that aRUN starts on 1978/05 we mean that it commences at the start of May 1978. Onthe other hand, if we say it ends on 1978/05 we mean it terminates at the end ofMay 1978. Thus, HSPF has two separate conventions for the external labeling oftime. When supplying values for a date/time field a user may omit any element inthe field except the year, which must be supplied as a 4 digit figure. HSPF willsubstitute the defaults given above for any blank or zero values. The completedstarting and ending date/time fields are translated into another format, whichis the only one used to label intervals and time points internally. It has aresolution of 1 minute. Thus, time is recorded as a year/month/day/hour/minuteset, to completely specify either a time interval or point. The date/time usedby the internal clock uses the "contained within" principle. For example, thefirst minute in an hour is numbered 1 (not 0) and the last is numbered 60 (not59). The same applies to the numbering of hours. Thus, the time conventionallylabeled 11:15 is in the 12th hour of the day so is labeled 12:15 internally; thelast minute of 1978 is labeled 1978/12/31 24:60. This convention is extended tothe labeling of points by labeling it with the minute that immediately precedesit. Thus, midnight New Year's eve 1978/1979 is 1978/12/31 24:60, not 1979/01/0100:00. This gives a system for uniquely labeling each point internally.
OUTLEV is a flag which governs the quantity of informative output produced bythe Run Interpreter. A value of 0 results in minimal output; 10 results in verydetailed output useful primarily for debugging the software. A value of 3 or 4is appropriate for most runs. OUTLEV does not affect error or warning messages.
SPOUT is a flag that governs the quantity of output produced in the RunInterpreter Output file whenever a Special Action is performed during thesimulation. A value of 1 results in minimal output, and a value of 10 resultsin maximum output.
RESMFG represents a feature that is not supported in this version of HSPF. Itshould be set to 0.
If RUNFG is 1, the system will both interpret the input and execute the RUN. Ifit is 0, only the interpretation will be done.
EMFG is the UCI units system flag for all simulation operations (i.e., PERLND,IMPLND and RCHRES operations). If EMFG is 1, all input values in the UCI filewill be interpreted using the English units defined in the tables in thissection (Part F) of this manual. If EMFG is 2, Metric units are assumed for UCIvalues.
FILES Block
293
4.2 FILES Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******FILES<ftyp> <un#> <-------filename----------------------------------------------->. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all files are specified). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END FILES
******* Example *******
FILES<FTYP> UNIT# FILE NAME ***WDM1 24 test.wdmMESSU 21 test.mes 61 test.dsp 33 test.plsEND FILES
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Comment Name(s)--------------------------------------------------------------------------------
<ftyp> FTYPE A6 File type; valid values are: MESSU, WDM, WDM1, WDM2, WDM3, WDM4, DSS1, DSS2, DSS3, DSS4, DSS5, PLTGEN (for VAX only), and blank (" ").
<un#> FUNIT I5 File unit number; valid values are 1-99 (21-99 recommended).
<filename> FNAME A64 File name; complete path name or local name if in default/current directory.
--------------------------------------------------------------------------------
FILES Block
294
Explanation
The FILES Block contains the names of input and output files used by the programduring the run; this block associates the unit numbers specified in various partsof the UCI file with actual disk file names. It is designed to eliminate the needfor a separate command file, such as a DOS batch (BAT) file or VAX command (COM)file, where the correspondence between file name and unit number is often performedfor batch programs such as HSPF. Since the FILES Block requires that the programbe able to locate the UCI file, HSPF prompts the user for its name. Alternatively,on DOS-based PCs, the command line for invoking HSPF may include the name of theUCI file. The syntax is as follows:
hspf uci-file-name <RET>
FTYPE is a keyword that identifies the type of file. There are twelve FTYPE's thatHSPF recognizes, and FTYPE must be specified for these types of files. Note,however, that the WDM and WDM1 keywords are synonymous, and should not appeartogether in the same FILES block. For all other files, this field should be leftblank. The FTYPE keyword should be left-justified in columns one through six. Thevalid FTYPE values are shown below:
DESCRIPTION FTYPE
Run interpreter output MESSU Watershed Data Management WDM, WDM1, WDM2, WDM3, WDM4 HEC Data Storage System files DSS1, DSS2, DSS3, DSS4, DSS5 VAX (only) PLTGEN output file PLTGEN Other input and output files (blank)
FUNIT is the file unit number of the file. This corresponds to the unit number ofthose files specified in other parts of the UCI file. FUNIT is an integer valuethat should be right-justified in columns 9 through 13; valid values are 1-99.Each value of FUNIT in the FILES Block should be unique, and the values 7 and 9 arereserved for internal scratch files. Preferably, values of 21 and greater shouldbe used to avoid any conflicts.
FNAME is the name of the file. If the file is not in the current (default)directory, the complete path name should be specified. FNAME should be left-justified in columns 17 through 80.
The FILES Block is usually required. In particular, if a WDM or DSS file is neededby the run, it must be specified in the FILES Block, since the program does nothave a default name for these files.
FILES Block
295
Similarly, for the operating modules (PERLND, IMPLND, RCHRES, DISPLY, PLTGEN,DURANL, and MUTSIN), and sequential (SEQ) time series input, the user must specifyfile unit numbers as the destination for printout (or source for MUTSIN orsequential time series input). Also, it is recommended that these files beexplicitly assigned names in the FILES Block. However, if the user does notinclude an entry in the FILES block for one of these operations, a file isautomatically opened by HSPF with the default name "hspfxx.dat", where xx is theunit number (except for the MESSU file, which defaults to "hspfecho.out").
OPN SEQUENCE Block
296
4.3 OPN SEQUENCE Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******OPN SEQUENCE [INGRP INDELT <idt>] <-opn-id-----> . . . . . . . <-opn-id-----> [END INGRP ] <-opn-id-----> INDELT <idt> . . . . . . . <-opn-id-----> INDELT <idt> [INGRP INDELT <idt>] <-opn-id-----> . . . . . . . [END INGRP ] . . . . . .END OPN SEQUENCE
******* Example ******* OPN SEQUENCE INGRP INDELT 02:00 PERLND 20 PERLND 21 PERLND 22 END INGRP RCHRES 1 INDELT 12:00 END OPN SEQUENCE********************************************************************************
Details -------------------------------------------------------------------------------- Symbol Fortran Format Comment Name(s)--------------------------------------------------------------------------------<idt> HRMIN(2) I2,1X,I2 Time interval (hour:min) used in the INPAD e.g., 00:05
<-opn-id-> OPTYP,OPTNO A6,5X,I3 Type and number of this operation. e.g., RCHRES 100 --------------------------------------------------------------------------------
OPN SEQUENCE Block
297
Explanation
This block specifies the various operations to be performed in the RUN and,optionally, their grouping into INGROUPs. The operations will be performed in thesequence specified in the block, apart from repetition implied by grouping. Amaximum of 200 operations can be specified in one run.
Every <-opn-id-> consists of OPTYP and OPTNO. The OPTYP field must contain anidentifier of up to 6 characters which corresponds to one of the operating moduleidentifiers in the HSPF system. The OPTNO field contains an integer whichdistinguishes operations of the same type from one another. Every <opn-id> (OPTYPplus OPTNO) must be unique.
The time intervals of the INGROUPs (or the RUN) are specified in this block. Theseappear on the INGROUP lines, except where the user has not placed an operation inan INGROUP. In that case <idt> is specified alongside <-opn-id->.
4.3.1 Optimization of Operation Sequencing
The sequence of operations within the Operations Sequence block should be optimizedto make most efficient use of the internal scratch pad (INPAD). Optimal use of theINPAD is accomplished by reducing the maximum number of time series (rows) on theINPAD. This increases the length of each row and the INSPAN, which reducesswapping between operations.
A time series occupies a row on the INPAD from the moment it is either read froman external source or is created by an operation until the moment it is used by thelast operation requiring it. HSPF automatically optimizes the reading of data fromexternal sources and writing of data to external targets. Optimal sequencing of operations requires that an operation be executed as soon asall input timeseries produced by other operations have been created. For example,a DISPLY operation which displays outflow from a PERLND operation shouldimmediately follow the PERLND operation. A RCHRES operation representing a sectionof stream should immediately follow any RCHRES operations representing reachesupstream and any PERLND operations which contribute local inflow.
For example, a watershed is represented by 4 PERLND operations, 5 RCHRESoperations, 2 PLTGEN operations, 4 DISPLY operations, and 1 DURANL operation.These are defined as follows:
OPN SEQUENCE Block
298
PERLND 1 - rain gage 1, land use of pasture PERLND 2 - rain gage 1, land use of corn PERLND 3 - rain gage 2, land use of pasture PERLND 4 - rain gage 2, land use of corn RCHRES 1 - local inflow from PERLND 1 and 2 RCHRES 2 - upstream inflow from RCHRES 1, local inflow from PERLND 1 and 2 RCHRES 3 - local inflow from PERLND 3 and 4 RCHRES 4 - upstream inflow from RCHRES 2 and 3, local inflow from PERLND 3 and 4 RCHRES 5 - upstream inflow from RCHRES 4, local inflow from PERLND 3 and 4 DISPLY 1 - outflow from RCHRES 5 DISPLY 2 - outflow from RCHRES 3 DISPLY 3 - unit flow from PERLND 2 DISPLY 4 - unit flow from PERLND 4 PLTGEN 1 - outflow from RCHRES 5, measured flow at bottom of RCHRES 5 PLTGEN 2 - outflow from RCHRES 1, area weighted sum of unit flow from PERLND 1 and 2 DURANL 1 - outflow from RCHRES 5
The optimum order for these operations is:
PERLND 1 PERLND 2 DISPLY 3 RCHRES 1 PLTGEN 2 RCHRES 2 PERLND 3 PERLND 4 DISPLY 4 RCHRES 3 DISPLY 2 RCHRES 4 RCHRES 5 DISPLY 1 DURANL 1 PLTGEN 1
Operation-type Block
299
4.4 <Operation-type> Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
<otyp> General input Section 1 input -- Section 2 input | Only supplied if the operating module contains sections . | and the section is active . | Section N input --END <otyp>
********************************************************************************
Details
-------------------------------------------------------------------------------- Symbol Fortran Format Comment Name(s)--------------------------------------------------------------------------------
<otyp> OPTYP A6 Type of operation covered in this block, e.g., RCHRES, PERLND
--------------------------------------------------------------------------------
Explanation
This type of block deals with data which pertain to all operations of the same<Operation-type>, e.g., the parameters and initial conditions for all the PerviousLand segments in a RUN. It is not concerned with relationships between operationsor with external sources or targets for time series. This type of block provides for general input and for input which is specific toindividual sections of the operating module. The latter only apply to moduleswhich are sectioned (PERLND, IMPLND, and RCHRES). The general input contains allof the information which simple (non-sectioned) modules require; for sectionedmodules it contains input which is not specific to any one section.
The general organization of the <Operation-type> blocks is as follows:
Operation-type Block
300
The user supplies his input in a set of tables (e.g., ACTIVITY, Sect 4.4(1).1.1below). Each table has a name (eg. ACTIVITY), called the "Table-type". A tablestarts with the heading <Table-type> and ends with the delimiter END <Table-type>.The body of the table consists of:
<range><------------values-------------------------->
where <range> is the range of operation-type numbers to which the <values> apply.If the second field in <range> is blank, the range is assumed to consist of asingle operation. Thus, in the example in Sect 4.4(1).1.1, Pervious Land-segments(PERLNDs) 1 through 7 have the same set of active sections, while PERLND 9 has adifferent set.
Thus, a table lists the values given to a specified set of variables (occupyingonly 1 line) for all the operations of a given type. The input was designed thisway to minimize the quantity of data supplied when many operations have the samevalues for certain sets of input.
HSPF will only look for a given Table-type if the options already specified by theuser require data contained within it. Thus, Table-type MON-INTERCEP (Sect4.4(1).4.6) is relevant only if VCSFG in Table-type PWAT-PARM1 (4.4(1).4.1) is setto 1 for one or more PERLNDs. The system has been designed to ignore redundantinformation. Thus, if VCSFG is 0 and Table-type MON-INTERCEP is supplied, the tablewill be ignored.
On the other hand, if an expected value is not supplied, the system will attemptto use a default value. This situation can arise in one of three ways:
1. The entire table may be missing from the UCI.
2. The table may be present but not contain an entry (line) for the operationin question. The example in Sect 4.4(1).1.1 has no entry for PLS No. 8.Thus, all values in its active sections vector will have the default of 0.
3. A field may be left blank. In the example in Section 4.4(1).4.2, KVARY willacquire the default value 0.0 for PLS's 1 through 7.
When appropriate, the HSPF system will also check that a value supplied by the useralls within an allowable range. If it does not, an error message is generated.
Note that a table contains either integers or real values, but generally not both.For example, Table-type ACTIVITY (Sect 4.4(1).1.1) contains only integer flags,while Table-type PWAT-PARM2 (4.4(1).4.2) contains only real numbers. For tablescontaining real-valued data, the documentation gives separate defaults, minima andmaxima for the English and Metric unit systems. The user specifies the unitssystem for the UCI in the GLOBAL block.
PERLND Block
301
4.4(1) PERLND Block ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PERLND General input [section ATEMP input] [section SNOW input] [section PWATER input] [section SEDMNT input] [section PSTEMP input] [section PWTGAS input] [section PQUAL input] [section MSTLAY input] [section PEST input] [section NITR input] [section PHOS input] [section TRACER input] END PERLND
********************************************************************************
Explanation
This block contains the data which are domestic to all the Pervious Land Segmentsin the RUN. The general input is always relevant: other input is only requiredif the module section concerned is active.
PERLND -- General Input
302
4.4(1).1 PERLND BLOCK -- General input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
Table-type ACTIVITY [Table-type PRINT-INFO] Table-type GEN-INFO
********************************************************************************
Explanation
The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
PERLND -- General Input
303
4.4(1).1.1 Table-type ACTIVITY -- Active Sections Vector
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
ACTIVITY <-range><-----------------a-s-vector-------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END ACTIVITY
******* Example *******
ACTIVITY <PLS > Active Sections *** # - # ATMP SNOW PWAT SED PST PWG PQAL MSTL PEST NITR PHOS TRAC*** 1 7 1 1 1 9 0 0 0 1 END ACTIVITY
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<a-s-vector> ASVEC(12) 12I5 0 0 1 --------------------------------------------------------------------------------
Explanation
The PERLND module is divided into 12 sections. The values supplied in this tablespecify which sections are active and which are not, for each operation involvingthe PERLND module. A value of 0 means inactive and 1 means active. Any meaningfulsubset of sections may be active.
PERLND -- General Input
304
4.4(1).1.2 Table-type PRINT-INFO -- Printout information for PERLND
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PRINT-INFO <-range><-----------------------print-flags------------------------><piv><pyr> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PRINT-INFO
******* Example *******
PRINT-INFO <PLS > ********************* Print-flags ************************* PIVL PYR # - # ATMP SNOW PWAT SED PST PWG PQAL MSTL PEST NITR PHOS TRAC ********* 1 7 2 4 6 4 3 2 10 12 END PRINT-INFO
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<print-flags> PFLAG(12) 12I5 4 2 6
<piv> PIVL I5 1 1 1440
<pyr> PYREND I5 9 1 12--------------------------------------------------------------------------------
PERLND -- General Input
305
Explanation
HSPF permits the user to vary the printout level (maximum frequency) for thevarious active sections of an operation. The meaning of each permissible value forPFLAG() is:
2 means every PIVL intervals 3 means every day 4 means every month 5 means every year 6 means never
In the example above, output from Pervious Land-segments 1 thru 7 will occur asfollows:
Section Maximum frequency
ATEMP 10 intervalsSNOW month PWATER never SEDMNT -- thru | month (defaulted)PEST -- NITR month PHOS day TRACER 10 intervals
A value need only be supplied for PIVL if one or more sections have a printoutlevel of 2. For those sections, printout will occur every PIVL intervals (that is,every PIVL*DELT minutes, where DELT is the number of minutes in the time step orthe RUN or INGROUP). PIVL must be chosen such that there are an integer number ofprintout periods in a day.
HSPF will automatically provide printed output at all standard intervals greaterthan the specified interval. In the above example, output for section PHOS willbe printed at the end of each day, month, and year. PYREND is the calendar month which will terminate the year for printout purposes.Thus, the annual summary can reflect the situation over the past water year or thepast calendar year, etc.
PERLND -- General Input
306
4.4(1).1.3 Table-type GEN-INFO -- Other general information for PERLND ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GEN-INFO <-range><---PLS-id---------> <unit-sys><-printu-> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GEN-INFO ******* Example ******* GEN-INFO <PLS > PLS Name Units Printout *** # - # t-series Engl Metr *** in out *** 1 Yosemite Valley 1 1 23 24 2 Kings river 1 1 23 24 END GEN-INFO ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<PLS-id> LSID(5) 5A4 none none none
<unit-sys> IUNITS,OUNITS 2I5 1 1 2 <printu> PUNIT(2) 2I5 0 0 99--------------------------------------------------------------------------------
PERLND -- General Input
307
Explanation Any string of up to 20 characters may be supplied as the identifier (LSID) for aPERLND. The values supplied for <unit-sys> indicate the system of units for data in theinput time series and output time series, respectively: 1 means English units, 2means Metric units.
Note: All operations in the run must use the same units system for data in the UCIfile; therefore, this system of units is specified by EMFG in the GLOBAL block.
The values supplied for PUNIT(*) indicate the destinations (files) of printout inEnglish and metric units, respectively. A value of 0 means no printout is requiredin that unit system. A non-zero value means printout is required in that system,and the value is the unit number of the file to which printout is to be written.The unit number is associated with a filename in the FILES BLOCK.
Note that printout for each Impervious Land Segment can be obtained in either theEnglish or Metric systems, or both (irrespective of the system used to supply theinputs).
PERLND -- Section ATEMP Input
308
4.4(1).2 PERLND BLOCK -- Section ATEMP input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type ATEMP-DAT] ******************************************************************************** Explanation The exact format of the table mentioned above is detailed in the documentationwhich follows. Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
PERLND -- Section ATEMP Input
309
4.4(1).2.1 Table-type ATEMP-DAT -- Elevation difference between gage & PLS ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** ATEMP-DAT <-range><el-diff-><-airtmp-> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END ATEMP-DAT ******* Example ******* ATEMP-DAT <PLS > El-diff *** # - # (ft) *** 1 7 150. END ATEMP-DAT ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<el-diff> ELDAT F10.0 0.0 none none ft Engl 0.0 none none m Metric <airtmp> AIRTMP F10.0 60 -60 140 Deg F Engl 15 -50 60 Deg C Metric-------------------------------------------------------------------------------- Explanation ELDAT is the difference in elevation between the temperature gage and the PERLND;it is used to estimate the temperature over the segment by application of a lapserate. ELDAT is positive if the segment is higher than the gage, and vice versa.
AIRTMP is the air temperature over the land segment at the start of the RUN.
PERLND -- Section SNOW Input
310
4.4(1).3 PERLND BLOCK -- Section SNOW input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type ICE-FLAG] Table-type SNOW-PARM1 [Table-type SNOW-PARM2] [Table-type SNOW-INIT1] [Table-type SNOW-INIT2] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
PERLND -- Section SNOW Input
311
4.4(1).3.1 Table-type ICE-FLAG -- governs simulation of ice formation in snow
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** ICE-FLAG <-range><ice> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END ICE-FLAG ******* Example ******* ICE-FLAG <PLS > Ice- *** # - # flag *** 1 7 1 END ICE-FLAG ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<ice> ICEFG I5 0 0 1 -------------------------------------------------------------------------------- Explanation A value of 0 means ice formation in the snow pack will not be simulated; 1 meansit will.
PERLND -- Section SNOW Input
312
4.4(1).3.2 Table-type SNOW-PARM1 -- First group of SNOW parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SNOW-PARM1 <-range><----------------snowparm1-----------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SNOW-PARM1 ******* Example ******* SNOW-PARM1 <PLS > Latitude Mean- SHADE SNOWCF COVIND *** # - # elev *** 1 7 39.5 3900. 0.3 1.2 10. END SNOW-PARM1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<snowparm1> LAT 5F10.0 40.0 -90.0 90.0 degrees Both MELEV 0.0 0.0 30000.0 ft Engl 0.0 0.0 10000.0 m Metric SHADE 0.0 0.0 1.0 none Both SNOWCF none 1.0 100.0 none Both COVIND none 0.01 none in Engl none 0.25 none mm Metric--------------------------------------------------------------------------------
PERLND -- Section SNOW Input
313
Explanation LAT is the latitude of the pervious land segment (PLS). It is positive for thenorthern hemisphere, and negative for the southern hemisphere.
MELEV is the mean elevation of the PLS above sea level.
SHADE is the fraction of the PLS which is shaded from solar radiation, by trees forexample.
SNOWCF is the factor by which the input precipitation data will be multiplied, ifthe simulation indicates it is snowfall, to account for poor catch efficiency ofthe gage under snow conditions.
COVIND is the maximum snowpack (water equivalent) at which the entire PLS will becovered with snow (see SNOW section in Functional Description). 4.4(1).3.3 Table-type SNOW-PARM2 -- Second group of SNOW parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SNOW-PARM2 <-range><--------------------snowparm2-----------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SNOW-PARM2 ******* Example ******* SNOW-PARM2 <PLS > *** # - # RDCSN TSNOW SNOEVP CCFACT MWATER MGMELT *** 1 7 0.2 33. END SNOW-PARM2
********************************************************************************
PERLND -- Section SNOW Input
314
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<snowparm2> RDCSN F10.0 0.15 0.01 1.0 none Both TSNOW F10.0 32.0 30.0 40.0 degF Engl 0.0 -1.0 5.0 degC Metric SNOEVP F10.0 0.1 0.0 1.0 none Both CCFACT F10.0 1.0 0.0 2.0 none Both MWATER F10.0 0.03 0.0 1.0 none Both MGMELT F10.0 0.01 0.0 1.0 in/day Engl 0.25 0.0 25. mm/day Metric-------------------------------------------------------------------------------- Explanation RDCSN is the density of cold, new snow relative to water. This value applies tosnow falling at air temperatures lower than or equal to 0 degrees F. At highertemperatures the density of snow is adjusted.
TSNOW is the air temperature below which precipitation will be snow, undersaturated conditions. Under non-saturated conditions the temperature is adjustedslightly. SNOEVP is a parameter which adapts the snow evaporation (sublimation) equation tofield conditions. CCFACT is a parameter which adapts the snow condensation/convection melt equationto field conditions. MWATER is the maximum water content of the snow pack, in depth of water per depthof water. MGMELT is the maximum rate of snowmelt by ground heat, in depth of water per day.This is the value which applies when the pack temperature is at the freezing point.
PERLND -- Section SNOW Input
315
4.4(1).3.4 Table-type SNOW-INIT1 -- First group of initial values for SNOW
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SNOW-INIT1 <-range><-------------------snowinit1------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SNOW-INIT1 ******* Example ******* SNOW-INIT1 <PLS > *** # - # Pack-snow Pack-ice Pack-watr RDENPF DULL PAKTMP*** 1 7 2.1 .02 .40 END SNOW-INIT1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<snowinit1> Pack-snow F10.0 0.0 0.0 none in Engl 0.0 0.0 none mm Metric Pack-ice F10.0 0.0 0.0 none in Engl 0.0 0.0 none mm Metric Pack-watr F10.0 0.0 0.0 none in Engl 0.0 0.0 none mm Metric RDENPF F10.0 0.2 .01 1.0 none Both DULL F10.0 400. 0.0 800. none Both PAKTMP F10.0 32. none 32. degF Engl 0.0 none 0.0 degC Metric--------------------------------------------------------------------------------
PERLND -- Section SNOW Input
316
Explanation
Pack-snow is the quantity of snow in the pack (water equivalent).
Pack-ice is the quantity of ice in the pack (water equivalent).
Pack-watr is the quantity of liquid water in the pack.
RDENPF is the density of the frozen contents (snow and ice) of the pack, relativeto water.
DULL is an index to the dullness of the snow pack surface, from which albedo isestimated.
PAKTMP is the mean temperature of the frozen contents of the snow pack.
PERLND -- Section SNOW Input
317
4.4(1).3.5 Table-type SNOW-INIT2 -- Second group of initial values for SNOW
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SNOW-INIT2 <-range><--------snowinit2-----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END SNOW-INIT2
******* Example ******* SNOW-INIT2 <PLS > *** # - # COVINX XLNMLT SKYCLR*** 1 7 0.50 END SNOW-INIT2 ********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<snowinit2> COVINX F10.0 0.01 0.01 none in Engl 0.25 0.25 none mm Metric XLNMLT F10.0 0.0 0.0 none in Engl 0.0 0.0 none mm Metric SKYCLR F10.0 1.0 .15 1.0 none Both-------------------------------------------------------------------------------- Explanation COVINX is the current snow pack depth (water equivalent) required to obtaincomplete areal coverage of the PLS. If the pack is less than this amount, arealcover is prorated (PACKF/COVINX). XLNMLT is the current remaining possible increment to ice storage in the pack (seeFunctional Description). It is relevant if ice formation is simulated (ICEFG= 1).
SKYCLR is the fraction of sky which is assumed to be clear at the present time.
PERLND -- Section PWATER Input
318
4.4(1).4 PERLND BLOCK -- Section PWATER input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type PWAT-PARM1] Table-type PWAT-PARM2 [Table-type PWAT-PARM3] Table-type PWAT-PARM4 [Table-type PWAT-PARM5] [Table-type MON-INTERCEP] -- [Table-type MON-UZSN] | [Table-type MON-MANNING] | only required if the relevant quantity [Table-type MON-INTERFLW] | varies through the year [Table-type MON-IRC] | [Table-type MON-LZETPARM] -- [Table-type PWAT-STATE1 ] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
PERLND -- Section PWATER Input
319
4.4(1).4.1 Table-type PWAT-PARM1 -- First group of PWATER parameters (flags)
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PWAT-PARM1 <-range><--------------pwatparm1-------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWAT-PARM1
******* Example ******* PWAT-PARM1 <PLS > Flags *** # - # CSNO RTOP UZFG VCS VUZ VNN VIFW VIRC VLE IFFC *** 1 7 1 1 END PWAT-PARM1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<pwatparm1> CSNOFG I5 0 0 1 RTOPFG I5 0 0 1 UZFG I5 0 0 1 VCSFG I5 0 0 1 VUZFG I5 0 0 1 VNNFG I5 0 0 1 VIFWFG I5 0 0 1 VIRCFG I5 0 0 1 VLEFG I5 0 0 1 IFFCFG I5 1 1 2--------------------------------------------------------------------------------
PERLND -- Section PWATER Input
320
Explanation If CSNOFG is 1, section PWATER assumes that snow accumulation and melt is beingconsidered. It will, therefore, expect that the time series produced by sectionSNOW are available, either internally (produced in this RUN) or from externalsources (e.g., produced in a previous RUN). If CSNOFG is 0, no such time seriesare expected. See the Functional Description for further information.
RTOPFG is a flag that selects the algorithm for computing overland flow. Twooptional methods are provided. If RTOPFG is 1, routing of overland flow is done inthe same way as in the predecessor models HSPX, ARM and NPS. A value of 0 resultsin a different algorithm (see Functional Description for details).
UZFG selects the method for computing inflow to the upper zone. If UZFG is 1,upper zone inflow is computed in the same way as in the predecessor models HSPX,ARM and NPS. A value of 0 results in the use of a different algorithm, which isless sensitive to changes in DELT (see functional description). The flags beginning with "V" indicate whether or not certain parameters will beassumed to vary through the year on a monthly basis: 1 means they do vary, 0 meansthey do not. The quantities which can vary on a monthly basis are:
VCSFG interception storage capacity VUZFG upper zone nominal storage VNNFG Manning's n for the overland flow plane VIFWFG interflow inflow parameter VIRCFG interflow recession constant VLEFG lower zone evapotranspiration (E-T) parameter
If any of these flags are on (1), monthly values for the parameter concerned mustbe supplied (see Table-types MON-xxx, documented later in this section).
If IFFCFG is 1, then the effect of frozen ground on infiltration rate is computedfrom the amount of ice in the snow pack (PACKI). CSNOFG must be turned on, and ifsection SNOW does not compute PACKI (because ICEFG is off or the section isinactive) PACKI must be supplied as an input time series. If IFFCFG is 2, then theinfiltration adjustment factor is determined from the soil temperature in the lowerlayer/groundwater layer, which is either computed in section PSTEMP or must besupplied as an input time series. (See Table-type PWAT-PARM5 for more details.)
PERLND -- Section PWATER Input
321
4.4(1).4.2 Table-type PWAT-PARM2 -- Second group of PWATER parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWAT-PARM2 <-range><--------------------------pwatparm2---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWAT-PARM2 ******* Example ******* PWAT-PARM2 <PLS > *** # - # ***FOREST LZSN INFILT LSUR SLSUR KVARY AGWRC 1 7 0.2 8.0 0.7 400. .001 .98 END PWAT-PARM2 ********************************************************************************
PERLND -- Section PWATER Input
322
Details
--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwatparm2> FOREST F10.0 0.0 0.0 1.0 none Both LZSN F10.0 none .01 100. in Engl none .25 2500. mm Metric INFILT F10.0 none 0.0001 100. in/hr Engl none 0.0025 2500. mm/hr Metric LSUR F10.0 none 1.0 none ft Engl none 0.3 none m Metric SLSUR F10.0 none .000001 10. none Both KVARY F10.0 0.0 0.0 none 1/in Engl 0.0 0.0 none 1/mm Metric AGWRC F10.0 none 0.001 0.999 1/day Both-------------------------------------------------------------------------------- Explanation FOREST is the fraction of the PLS which is covered by forest, and which willtherefore continue to transpire in winter. This is only relevant if snow is beingconsidered (i.e., CSNOFG = 1). LZSN is the lower zone nominal storage. INFILT is an index to the infiltration capacity of the soil. LSUR is the length of the assumed overland flow plane.
SLSUR is the slope of the overland flow plane. KVARY is a parameter which affects the behavior of groundwater recession flow,enabling it to be non-exponential in its decay with time.
AGWRC is the basic groundwater recession rate if KVARY is zero and there is noinflow to groundwater; AGWRC is defined as the rate of flow today divided by therate of flow yesterday.
PERLND -- Section PWATER Input
323
4.4(1).4.3 Table-type PWAT-PARM3 -- Third group of PWATER parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWAT-PARM3 <-range><------------------------pwatparm3-----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWAT-PARM3 ******* Example ******* PWAT-PARM3 <PLS >*** # - #*** PETMAX PETMIN INFEXP INFILD DEEPFR BASETP AGWETP 1 7 9 39 33 3.0 1.5 END PWAT-PARM3 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwatparm3> PETMAX F10.0 40. none none degF Engl 4.4 none none degC Metric PETMIN F10.0 35. none none degF Engl 1.7 none none degC Metric INFEXP F10.0 2.0 0.0 10.0 none Both INFILD F10.0 2.0 1.0 2.0 none Both DEEPFR F10.0 0.0 0.0 1.0 none Both BASETP F10.0 0.0 0.0 1.0 none Both AGWETP F10.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
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Explanation PETMAX is the air temperature below which E-T will arbitrarily be reduced below thevalue obtained from the input time series, and PETMIN is the temperature belowwhich E-T will be zero regardless of the value in the input time series. Thesevalues are only used if snow is being considered (CSNOFG= 1).
INFEXP is the exponent in the infiltration equation, and INFILD is the ratiobetween the maximum and mean infiltration capacities over the PLS.
DEEPFR is the fraction of groundwater inflow which will enter deep (inactive)groundwater, and, thus, be lost from the system as it is defined in HSPF.
BASETP is the fraction of remaining potential E-T which can be satisfied frombaseflow (groundwater outflow), if enough is available. AGWETP is the fraction of remaining potential E-T which can be satisfied fromactive groundwater storage if enough is available. 4.4(1).4.4 Table-type PWAT-PARM4 -- Fourth group of PWATER parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWAT-PARM4 <-range><--------------------pwatparm4-----------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWAT-PARM4 Example ******* PWAT-PARM4 <PLS > *** # - # CEPSC UZSN NSUR INTFW IRC LZETP*** 1 7 0.1 1.3 0.1 3. 0.5 0.7 END PWAT-PARM4
********************************************************************************
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Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwatparm4> CEPSC F10.0 0.0 0.0 10.0 in Engl 0.0 0.0 250. mm Metric UZSN F10.0 none 0.01 10.0 in Engl none 0.25 250. mm Metric NSUR F10.0 0.1 0.001 1.0 complex Both INTFW F10.0 none 0.0 none none Both IRC F10.0 none 1.0E-30 0.999 1/day Both LZETP F10.0 0.0 0.0 0.999 none Both-------------------------------------------------------------------------------- Explanation
Values in this table need only be supplied for those parameters which do not varythrough the year. If they do vary (as specified in Table-type PWAT-PARM1), monthlyvalues are supplied in the tables documented below (MON-xxx).
CEPSC is the interception storage capacity. UZSN is the upper zone nominal storage. NSUR is Manning's n for the assumed overland flow plane. INTFW is the interflow inflow parameter. IRC is the interflow recession parameter. Under zero inflow, this is the ratio oftoday's interflow outflow rate to yesterday's rate.
LZETP is the lower zone E-T parameter. It is an index to the density of deep-rooted vegetation.
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4.4(1).4.5 Table-type PWAT-PARM5 -- Fifth group of PWATER parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWAT-PARM5 <-range><---pwatparm5------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWAT-PARM5 ******* Example ******* PWAT-PARM3 <PLS >*** # - #*** FZG FZGL 1 7 9 0.9 0.1 END PWAT-PARM3 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwatparm5> FZG F10.0 1.0 0.0001 none /in Engl F10.0 0.0394 0.0001 none /mm Metr
FZGL F10.0 0.1 0.0001 1.0 none Both--------------------------------------- -----------------------------------------Explanation FZG is the parameter that adjusts for the effect of ice in the snow pack oninfiltration when IFFCFG is 1. It is not used if IFFCFG is 2.
FZGL is the lower limit of INFFAC as adjusted by ice in the snow pack when IFFCFGis 1. If IFFCFG is 2, FZGL is the value of INFFAC when the lower layer temperatureis at or below freezing.
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4.4(1).4.6 Table-type MON-INTERCEP -- Monthly interception storage capacity ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-INTERCEP <-range><-----------------mon-icep---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-INTERCEP ******* Example ******* MON-INTERCEP <PLS > Interception storage capacity at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .02 .03 .03 .04 .05 .08 .12 .15 .12 .05 .03 .01 END MON-INTERCEP ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-icep> CEPSCM(12) 12F5.0 0.0 0.0 10. in Engl 0.0 0.0 250. mm Metric-------------------------------------------------------------------------------- Explanation Monthly values of interception storage. Only required if VCSFG is 1 in Table-typePWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
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4.4(1).4.7 Table-type MON-UZSN -- Monthly upper zone nominal storage
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-UZSN <-range><------------------mon-uzsn--------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-UZSN ******* Example ******* MON-UZSN <PLS > Upper zone storage at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 7 .30 .35 .30 .45 .56 .57 .45 .67 .64 .54 .56 .40 END MON-UZSN ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-uzsn> UZSNM(12) 12F5.0 none .01 10. in Engl none .25 250. mm Metric-------------------------------------------------------------------------------- Explanation Monthly values of upper zone nominal storage. This table is only required if VUZFGis 1 in Table-type PWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWATER Input
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4.4(1).4.8 Table-type MON-MANNING -- Monthly Manning's n values ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-MANNING <-range><----------------mon-Manning-------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-MANNING ******* Example ******* MON-MANNING <PLS > Manning's n at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 7 .23 .34 .34 .35 .28 .35 .37 .35 .28 .29 .30 .30 END MON-MANNING ******************************************************************************** Details --------------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------- <mon-Manning> NSURM(12) 12F5.0 .10 .001 1.0 complex Both--------------------------------------------------------------------------- Explanation Monthly values of Manning's constant for overland flow. This table is onlyrequired if VNNFG is 1 in Table-type PWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWATER Input
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4.4(1).4.9 Table-type MON-INTERFLW -- monthly interflow inflow parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-INTERFLW <-range><----------------mon-interflw------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-INTERFLW ******* Example ******* MON-INTERFLW <PLS > Interflow inflow parameter for start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 7 2.0 3.3 3.6 3.8 4.2 5.6 5.6 7.6 7.5 5.6 4.6 3.4 END MON-INTERFLW ******************************************************************************** Details ----------------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------------------------- <mon-interflw> INTFWM(12) 12F5.0 none 0.0 none none Both----------------------------------------------------------------------------- Explanation Monthly values of the interflow inflow parameter. This table is only required ifVIFWFG is 1 in Table-type PWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWATER Input
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4.4(1).4.10 Table-type MON-IRC -- Monthly interflow recession constants
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-IRC <-range><--------------mon-irc-------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-IRC ******* Example ******* MON-IRC <PLS > Interflow recession constant at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .35 .40 .40 .40 .40 .43 .45 .45 .50 .45 .45 .40 END MON-IRC ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-irc> IRCM(12) 12F5.0 none 1.0E-30 0.999 /day Both-------------------------------------------------------------------------------- Explanation Monthly values of the interflow recession parameter. This table is only requiredif VIRCFG is 1 in Table-type PWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWATER Input
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4.4(1).4.11 Table-type MON-LZETPARM -- Monthly lower zone E-T parameter ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-LZETPARM <-range><---------------mon-lzetparm-------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-LZETPARM ******* Example ******* MON-LZETPARM <PLS > Lower zone evapotranspiration parm at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .30 .30 .35 .35 .40 .40 .45 .45 .45 .45 .42 .38 END MON-LZETPARM ******************************************************************************** Details ----------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system ----------------------------------------------------------------------------<mon-lzetparm> LZETPM(12) 12F5.0 0.0 0.0 0.999 none Both ---------------------------------------------------------------------------- Explanation Monthly values of the lower zone ET parameter. This table is only required ifVLEFG is 1 in Table-type PWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWATER Input
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4.4(1).4.12 Table-type PWAT-STATE1 -- PWATER initial state variables
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWAT-STATE1 <-range><--------------pwat-state1-------------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWAT-STATE1 ******* Example ******* PWAT-STATE1 <PLS > PWATER state variables*** # - #*** CEPS SURS UZS IFWS LZS AGWS GWVS 1 7 0.05 0.10 0.25 0.01 8.2 2.0 .025 END PWAT-STATE1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwat-state1> CEPS 7F10.0 0.0 0.0 100 inches Engl 0.0 0.0 2500 mm Metric SURS 0.0 0.0 100 inches Engl 0.0 0.0 2500 mm Metric UZS .001 .001 100 inches Engl .025 .025 2500 mm Metric IFWS 0.0 0.0 100 inches Engl 0.0 0.0 2500 mm Metric LZS .001 .001 100 inches Engl .025 .025 2500 mm Metric AGWS 0.0 0.0 100 inches Engl 0.0 0.0 2500 mm Metric GWVS 0.0 0.0 100 inches Engl 0.0 0.0 2500 mm Metric--------------------------------------------------------------------------------
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Explanation This table is used to specify the initial water storages in the soil. CEPS is the initial interception storage.
SURS is the initial surface (overland flow) storage.
UZS is the initial upper zone storage.
IFWS is the initial interflow storage.
LZS is the initial lower zone storage.
AGWS is the initial active groundwater storage.
GWVS is the initial index to groundwater slope; it is a measure of antecedentactive groundwater inflow.
PERLND -- Section SEDMNT Input
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4.4(1).5 PERLND BLOCK -- Section SEDMNT input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type SED-PARM1] Tables in brackets [] are Table-type SED-PARM2 not always required. Table-type SED-PARM3 [Table-type MON-COVER] [Table-type MON-NVSI] [Table-type SED-STOR] ********************************************************************************
Explanation
The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
PERLND -- Section SEDMNT Input
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4.4(1).5.1 Table-type SED-PARM1 -- First group of SEDMNT parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SED-PARM1 <-range><--sed-parm1--> . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . END SED-PARM1
Example *******
SED-PARM1 <PLS >*** # - # CRV VSIV SDOP*** 1 7 0 1 0 END SED-PARM1
********************************************************************************
Details ----------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------- <sed-parm1> CRVFG 3I5 0 0 1 VSIVFG 0 0 2 SDOPFG 0 0 1 -----------------------------------------------------------
Explanation
If CRVFG is 1, erosion-related cover may vary throughout the year. Values aresupplied in Table-type MON-COVER. If VSIVFG is 1, the rate of net vertical sediment input may vary throughout theyear. If VSIVFG is 2, the vertical sediment input is added to the detachedsediment storage only on days when no rainfall occurred during the previous day.Values are supplied in Table-type MON-NVSI.
SDOPFG is a flag that determines the algorithm used to simulate removal of sedimentfrom the land surface. If SDOPFG is 1, sediment removal will be simulated with thealgorithm used in the predecessor models ARM and NPS. If it is 0, a differentalgorithm will be used (see the Functional Description for details).
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4.4(1).5.2 Table-type SED-PARM2 -- Second group of SEDMNT parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SED-PARM2 <-range><---------------sed-parm2----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SED-PARM2
******* Example *******
SED-PARM2 <PLS >*** # - # SMPF KRER JRER AFFIX COVER NVSI*** 1 7 0.9 0.08 1.90 0.01 0.5 -0.100 END SED-PARM2
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sedparm2> SMPF 6F10.0 1.0 0.001 1.0 none Both KRER 0.0 0.0 none complex Both JRER none none none complex Both AFFIX 0.0 0.0 1.0 /day Both COVER 0.0 0.0 1.0 none Both NVSI 0.0 none none lb/ac/day Engl 0.0 none none kg/ha/day Metric--------------------------------------------------------------------------------
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Explanation SMPF is a "supporting management practice factor." It is used to simulate thereduction in erosion achieved by use of erosion control practices.
KRER is the coefficient in the soil detachment equation.
JRER is the exponent in the soil detachment equation.
AFFIX is the fraction by which detached sediment storage decreases each day as aresult of soil compaction.
COVER is the fraction of land surface which is shielded from rainfall erosion (notconsidering snow cover, which is handled by the program).
NVSI is the rate at which sediment enters detached storage from the atmosphere.A negative value can be supplied, for example, to simulate removal by humanactivity or wind. If monthly values for COVER and NVSI are being supplied, values supplied for thesevariables in this table are not relevant.
PERLND -- Section SEDMNT Input
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4.4(1).5.3 Table-type SED-PARM3 -- Third group of SEDMNT parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SED-PARM3 <-range><------------sed-parm3-----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END SED-PARM3 ******* Example ******* SED-PARM3 <PLS >*** # - # KSER JSER KGER JGER*** 1 7 0.08 1.7 0.06 1.4 END SED-PARM3 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sedparm3> KSER 4F10.0 0.0 0.0 none complex Both JSER none none none complex Both KGER 0.0 0.0 none complex Both JGER none none none complex Both-------------------------------------------------------------------------------- Explanation KSER and JSER are the coefficient and exponent in the detached sediment washoffequation.
KGER and JGER are the coefficient and exponent in the matrix soil scour equation,which simulates gully erosion.
PERLND -- Section SEDMNT Input
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4.4(1).5.4 Table-type MON-COVER -- Monthly erosion-related cover values
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-COVER <-range><---------------mon-cover----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-COVER ******* Example ******* MON-COVER <PLS > Monthly values for erosion related cover *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 0.0 .12 .12 .24 .24 .56 .67 .56 .34 .34 .23 .12 END MON-COVER ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-cover> COVERM(12) 12F5.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
Explanation Monthly values of the COVER parameter. This table is only required if CRVFG is 1in Table-type SED-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section SEDMNT Input
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4.4(1).5.5 Table-type MON-NVSI -- Monthly net vertical sediment input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-NVSI <-range><---------------mon-nvsi-----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NVSI ******* Example ******* MON-NSVI <PLS > Monthly net vertical sediment input*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 -.01 -.02 -.03 -.04 -.05 -.03 -.02 -.01 0.0 .01 .03 .01 END MON-NVSI ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-nvsi> NVSIM(12) 12F5.0 0.0 none none lb/ac/day Engl 0.0 none none kg/ha/day Metric-------------------------------------------------------------------------------- Explanation Monthly values of the net vertical sediment input. This table is only required ifVSIVFG is greater than 0 in Table-type SED-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section SEDMNT Input
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4.4(1).5.6 Table-type SED-STOR -- Initial storage of detached sediment
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SED-STOR <-range><--------> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END SED-STOR ******* Example ******* SED-STOR <PLS > Detached sediment storage (tons/acre) *** # - # *** 1 7 0.2 END SED-STOR ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sed-stor> DETS F10.0 0.0 0.0 none tons/ac Engl 0.0 0.0 none tonnes/ha Metric--------------------------------------------------------------------------------
Explanation DETS is the initial storage of detached sediment.
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4.4(1).6 PERLND BLOCK -- Section PSTEMP input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type PSTEMP-PARM1] Table-type PSTEMP-PARM2 Tables in brackets [] are [Table-type MON-ASLT] not always required [Table-type MON-BSLT] [Table-type MON-ULTP1] [Table-type MON-ULTP2] [Table-type MON-LGTP1] [Table-type MON-LGTP2] [Table-type PSTEMP-TEMPS] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
PERLND -- Section PSTEMP Input
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4.4(1).6.1 Table-type PSTEMP-PARM1 -- Flags for section PSTEMP ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PSTEMP-PARM1 <-range><---pstemp-parm1---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END PSTEMP-PARM1 ******* Example ******* PSTEMP-PARM1 <PLS > Flags for section PSTEMP*** # - # SLTV ULTV LGTV TSOP*** 1 7 0 0 0 1 END PSTEMP-PARM1 ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<pstemp-parm1> SLTVFG 4I5 0 0 1 ULTVFG 0 0 1 LGTVFG 0 0 1 TSOPFG 0 0 2 ----------------------------------------------------------
PERLND -- Section PSTEMP Input
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Explanation If SLTVFG is 1, parameters for estimating surface layer temperature can varythroughout the year. Thus, Table-types MON-ASLT and MON-BSLT will be expected. ULTVFG serves the same purpose for upper layer temperature calculations. TablesMON-ULTP1 and MON-ULTP2 will be expected of ULTVFG is 1. LGTVFG serves the samepurpose for the lower layer and active groundwater layer temperature calculations.Table-types MON-LGTP1 and MON-LGTP2 will be expected if LGTVFG is 1. TSOPFG governs the methods used to estimate subsurface soil temperatures. IfTSOPFG is 0, they are computed using a mean departure from air temperature,together with smoothing factors. If TSOPFG is 2, the method is identical, exceptthat the lower layer/groundwater layer temperature is calculated from the upperlayer soil temperature, instead of directly from the air temperature. If TSOPFGis 1, upper layer soil temperature is estimated by regression on air temperature(like surface temperature). The lower layer/ground-water layer temperature issupplied directly by the user (a different value may be specified for each month).
4.4(1).6.2 Table-type PSTEMP-PARM2 -- Second group of PSTEMP parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PSTEMP-PARM2 <-range><--------------pstemp-parm2--------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PSTEMP-PARM2 ******* Example ******* PSTEMP-PARM2 <PLS >*** # - # ASLT BSLT ULTP1 ULTP2 LGTP1 LGTP2*** 1 7 24. .5 24. .5 40. 0.0 END PSTEMP-PARM2 ********************************************************************************
PERLND -- Section PSTEMP Input
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Details
--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pstemp-parm2> ASLT 6F10.0 32. 0.0 100. deg F Engl 0. -18. 38. deg C Metric BSLT 1.0 0.001 2.0 deg F/F Engl 1.0 0.001 2.0 deg C/C Metric Definition of remaining quantities depends on soil temperature option flag (TSOPFG in Table-type PSTEMP-PARM1)
TSOPFG = 0 or 2: ULTP1 none none none none Both ULTP2 none none none F deg Engl none none none C deg Metric LGTP1 none none none none Both LGTP2 none none none F deg Engl none none none C deg Metric TSOPFG = 1: ULTP1 none none none Deg F Engl none none none Deg C Metric ULTP2 none none none Deg F/F Engl none none none Deg C/C Metric LGTP1 none none none Deg F Engl none none none Deg C Metric LGTP2 not used-------------------------------------------------------------------------------- Explanation ASLT is the surface layer temperature when the air temperature is 32 degrees F (0degrees C). It is the intercept of the surface layer temperature regressionequation.
BSLT is the slope of the surface layer temperature regression equation.
If TSOPFG = 0 then:
ULTP1 is the smoothing factor in the upper layer temperature calculation.
ULTP2 is the mean difference between upper layer soil temperature and air temperature.
LGTP1 and LGTP2 are the smoothing factor and mean departure from air temperature for calculating lower layer/groundwater soil temperature.
PERLND -- Section PSTEMP Input
347
If TSOPFG = 1 then:
ULTP1 and ULTP2 are the intercept and slope in the upper layer soil temperatureregression equation (like ASLT and BSLT for the surface layer). LGTP1 is thelower layer/groundwater layer soil temperature. LGTP2 is not used.
If TSOPFG = 2 then:
ULTP1 is the smoothing factor in the upper layer temperature calculation.
ULTP2 is the mean difference between upper layer soil temperature and air temperature.
LGTP1 and LGTP2 are the smoothing factor and mean departure from the upper layer soil temperature for calculating lower layer/groundwater soil temperature.
If monthly values are being supplied for any of these quantities (in Table-typeMON-xxx), the value appearing in this table is not relevant.
PERLND -- Section PSTEMP Input
348
4.4(1).6.3 Table-type MON-ASLT -- Monthly values for ASLT
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-ASLT <-range><-----------------mon-aslt---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-ASLT ******* Example ******* MON-ASLT <PLS > Value of ASLT at start of each month (deg F)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 37. 38. 39. 40. 41. 42. 43. 44. 45. 44. 41. 40. END MON-ASLT ********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-aslt> ASLTM(12) 12F5.0 32. 0. 100. deg F Engl 0. -18. 38. deg C Metric--------------------------------------------------------------------------------Explanation This table is only required if SLTVFG is 1 in Table-type PSTEMP-PARM1.
The input monthly values apply to the first day of the month; values for inter-mediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PSTEMP Input
349
4.4(1).6.4 Table-type MON-BSLT -- Monthly values for BSLT ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-BSLT <-range><-----------------mon-bslt---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-BSLT ******* Example ******* MON-BSLT <PLS > Value of BSLT at start of each month (deg F/F)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .3 .3 .3 .4 .4 .5 .5 .5 .4 .4 .4 .3 END MON-BSLT ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-bslt> BSLTM(12) 12F5.0 1.0 0.001 2.0 deg F/F Engl 1.0 0.001 2.0 deg C/C Metric-------------------------------------------------------------------------------- Explanation This table is only required if SLTVFG is 1 in Table-type PSTEMP-PARM1. Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PSTEMP Input
350
4.4(1).6.5 Table-type MON-ULTP1 -- Monthly values for ULTP1
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-ULTP1 <-range><----------------mon-ultp1---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-ULTP1 ******* Example ******* MON-ULTP1 <PLS > Value of ULTP1 at start of each month (TSOPFG=1) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 37. 38. 39. 40. 42. 44. 47. 44. 42. 39. 39. 39. END MON-ULTP1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<mon-ultp1> ULTP1M(12) 12F5.0 see notes for Table-type PSTEMP-PARM2 -------------------------------------------------------------------------------- Explanation This table is only required if ULTVFG is 1 in Table-type PSTEMP-PARM1. Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PSTEMP Input
351
4.4(1).6.6 Table-type MON-ULTP2 -- Monthly values for ULTP2 ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-ULTP2 <-range><---------------mon-ultp2----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-ULTP2 ******* Example ******* MON-ULTP2 <PLS > Value of ULTP2 at start of each month (TSOPFG=1) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .3 .3 .4 .5 .5 .5 .6 .6 .5 .4 .4 .3 END MON-ULTP2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<mon-ultp2> ULTP2M(12) 12F5.0 see notes for Table-type PSTEMP-PARM2 -------------------------------------------------------------------------------- Explanation This table is only required if ULTVFG is 1 in Table-type PSTEMP-PARM1. Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PSTEMP Input
352
4.4(1).6.7 Table-type MON-LGTP1 -- Monthly values for LGTP1 ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-LGTP1 <-range><---------------mon-lgtp1----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-LGTP1 ******* Example ******* MON-LGTP1 <PLS > Value of LGTP1 at start of each month (TSOPFG=1) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 35. 38. 41. 43. 51. 45. 46. 45. 39. 37. 35. 35. END MON-LGTP1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------------------------------<mon-lgtp1> LGTP1M(12) 12F5.0 see notes for Table-type PSTEMP-PARM2 -------------------------------------------------------------------------------- Explanation This table is only required if LGTVFG is 1 in Table-type PSTEMP-PARM1. Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PSTEMP Input
353
4.4(1).6.8 Table-type MON-LGTP2 -- Monthly values for LGTP2 ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-LGTP2 <-range><---------------mon-lgtp2----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-LGTP2 ******* Example ******* MON-LGTP2 <PLS > Value for LGTP2 at start of each month (F deg) (TSOPFG=0) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 2.0 2.0 2.0 2.0 1.0 1.0 1.0 0.0 0.0 0.0 1.0 2.0 END MON-LGTP2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-lgtp2> LGTP2M(12) 12F5.0 none none none F deg Engl none none none C deg Metric-------------------------------------------------------------------------------- Explanation This table is only required if LGTVFG is 1 in Table-type PSTEMP-PARM1, and TSOPFGis 0 or 2. Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PSTEMP Input
354
4.4(1).6.9 Table-type PSTEMP-TEMPS -- Initial temperatures ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PSTEMP-TEMPS <-range><------------pstemp-temps--------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PSTEMP-TEMPS Example ******* PSTEMP-TEMPS <PLS > Initial temperatures*** # - # AIRTC SLTMP ULTMP LGTMP*** 1 7 48. 48. 48. 48. END PSTEMP-TEMPS ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pstemp-temps> AIRTC 4F10.0 60. -20. 120. deg F Engl 16. -29. 49. deg C Metric SLTMP 60. -20. 120. deg F Engl 16. -29. 49. deg C Metric ULTMP 60. -20. 120. deg F Engl 16. -29. 49. deg C Metric LGTMP 60. -20. 120. deg F Engl 16. -29. 49. deg C Metric-------------------------------------------------------------------------------- Explanation These are the initial temperatures: AIRTC - air temperature SLTMP - surface layer soil temperature ULTMP - upper layer soil temperature LGTMP - lower layer/groundwater layer soil temperature
PERLND -- Section PWTGAS Input
355
4.4(1).7 PERLND BLOCK -- Section PWTGAS input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type PWT-PARM1] [Table-type PWT-PARM2] Tables in brackets [] are not [Table-type MON-IFWDOX] always required [Table-type MON-IFWCO2] [Table-type MON-GRNDDOX] [Table-type MON-GRNDCO2] [Table-type PWT-TEMPS] [Table-type PWT-GASES] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
PERLND -- Section PWTGAS Input
356
4.4(1).7.1 Table-type PWT-PARM1 -- Flags for section PWTGAS ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWT-PARM1 <-range><----pwt-parm1-----> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END PWT-PARM1 Example ******* PWT-PARM1 <PLS > Flags for section PWTGAS*** # - # IDV ICV GDV GVC*** 1 7 0 0 1 0 END PWT-PARM1 ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)---------------------------------------------------------- <pwt-parm1> IDVFG 4I5 0 0 1 ICVFG 0 0 1 GDVFG 0 0 1 GCVFG 0 0 1 ---------------------------------------------------------- Explanation Each of these flags indicate whether or not a parameter is allowed to varythroughout the year, and thus, whether or not the corresponding table of monthlyvalues will be expected: FLAG PARAMETER TABLE FOR MONTHLY VALUES
IDVFG Interflow dissolved oxygen concentration MON-IFWDOX ICVFG Interflow CO2 concentration MON-IFWCO2 GDVFG Groundwater dissolved oxygen concentration MON-GRNDDOXGCVFG Groundwater CO2 concentration MON-GRNDCO2
PERLND -- Section PWTGAS Input
357
4.4(1).7.2 Table-type PWT-PARM2 -- Second group of PWTGAS parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWT-PARM2 <-range><-----------------pwt-parm2----------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWT-PARM2 Example ******* PWT-PARM2 <PLS > Second group of PWTGAS parameters *** # - # ELEV IDOXP ICO2P ADOXP ACO2P*** 1 7 1281. 8.2 0.2 8.2 0.3 END PWT-PARM2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwt-parm2> ELEV 5F10.0 0.0 -1000. 30000. ft Engl 0.0 -300. 9100. m Metric IDOXP 0.0 0.0 20. mg/l Both ICO2P 0.0 0.0 1.0 mg C/l Both ADOXP 0.0 0.0 20. mg/l Both ACO2P 0.0 0.0 1.0 mg C/l Both--------------------------------------------------------------------------------
Explanation
ELEV is the elevation of the PLS above sea level; it is used to adjust thesaturation concentrations of dissolved gases in surface outflow.
IDOXP is the concentration of dissolved oxygen in interflow outflow.ICO2P is the concentration of dissolved CO2 in interflow outflow. ADOXP is the concentration of dissolved oxygen in active groundwater outflow. ACO2P is the concentration of dissolved CO2 in active groundwater outflow.
PERLND -- Section PWTGAS Input
358
4.4(1).7.3 Table-type MON-IFWDOX -- Monthly interflow dissolved oxygen concentration ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-IFWDOX <-range><----------------mon-ifwdox--------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-IFWDOX ******* Example ******* MON-IFWDOX <PLS > Value at start of each month for interflow DO concentration*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 4.5 4.7 5.7 6.5 7.6 7.6 7.4 6.3 4.3 5.3 4.3 3.5 END MON-IFWDOX ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-ifwdox> IDOXPM(12) 12F5.0 0.0 0.0 20.0 mg/l Both-------------------------------------------------------------------------------- Explanation This table is only required if IDVFG is 1 in Table-type PWT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWTGAS Input
359
4.4(1).7.4 Table-type MON-IFWCO2 -- Monthly interflow CO2 concentration ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-IFWCO2 <-range><---------------mon-ifwco2---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-IFWCO2 ******* Example ******* MON-IFWCO2 <PLS > Value at start of each month for interflow CO2 concentration*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .123 .171 .142 .145 .157 .178 .122 .123 .143 .145 .176 .145 END MON-IFWCO2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-ifwco2> ICO2PM(12) 12F5.0 0.0 0.0 1.0 mg C/l Both-------------------------------------------------------------------------------- Explanation This table is only required if ICVFG is 1 in Table-type PWT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWTGAS Input
360
4.4(1).7.5 Table-type MON-GRNDDOX -- Monthly groundwater dissolved oxygen concentration ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-GRNDDOX <-range><---------------mon-grnddox--------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-GRNDDOX ******* Example ******* MON-GRNDDOX <PLS > Value at start of each month for groundwater DO concentration*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 4.5 4.7 4.9 4.9 4.9 4.9 5.0 5.6 5.7 5.8 5.4 5.1 END MON-GRNDDOX ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-grnddox> ADOXPM(12) 12F5.0 0.0 0.0 20.0 mg/l Both-------------------------------------------------------------------------------- Explanation This table is only required if GDVFG is 1 in Table-type PWT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWTGAS Input
361
4.4(1).7.6 Table-type MON-GRNDCO2 -- Monthly groundwater CO2 concentration ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-GRNDCO2 <-range><--------------mon-grndco2---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-GRNDCO2 ******* Example ******* MON-GRNDCO2 <PLS > Value at start of each month for groundwater CO2 concentration*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .23 .22 .22 .23 .24 .25 .24 .23 .22 .22 .22 .22 END MON-GRNDCO2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-grndco2> ACO2PM(12) 12F5.0 0.0 0.0 1.0 mg C/l Both-------------------------------------------------------------------------------- Explanation This table is only required if GCVFG is 1 in Table-type PWT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PWTGAS Input
362
4.4(1).7.7 Table-type PWT-TEMPS -- Initial water temperatures ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWT-TEMPS <-range><----------pwt-temps---------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PWT-TEMPS ******* Example ******* PWT-TEMPS <PLS > Initial water temperatures*** # - # SOTMP IOTMP AOTMP*** 1 7 47. 47. 53. END PWT-TEMPS ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwt-temps> SOTMP 3F10.0 60. 32. 100. deg F Engl 16. 0. 38. deg C Metric IOTMP 60. 32. 100. deg F Engl 16. 0. 38. deg C Metric AOTMP 60. 32. 100. deg F Engl 16. 0. 38. deg C Metric-------------------------------------------------------------------------------- Explanation These are the initial values of outflow water temperatures: SOTMP is surface outflow temperature. IOTMP is interflow outflow temperature. AOTMP is active groundwater outflow temperature.
PERLND -- Section PWTGAS Input
363
4.4(1).7.8 Table-type PWT-GASES -- Initial dissolved oxygen and CO2 concentrations
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PWT-GASES <-range><---------------pwt-gases----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PWT-GASES ******* Example ******* PWT-GASES <PLS > Initial DO and CO2 concentrations*** # - # SODOX SOCO2 IODOX IOCO2 AODOX AOCO2*** 1 7 8.9 .122 7.8 .132 3.5 .132 END PWT-GASES ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<pwt-gases> SODOX 6F10.0 0.0 0.0 20. mg/l Both SOCO2 0.0 0.0 1.0 mg C/l Both IODOX 0.0 0.0 20. mg/l Both IOCO2 0.0 0.0 1.0 mg C/l Both AODOX 0.0 0.0 20. mg/l Both AOCO2 0.0 0.0 1.0 mg C/l Both-------------------------------------------------------------------------------- Explanation These are the initial concentrations of dissolved gases in outflow: SODOX is DO concentration in surface outflow. SOCO2 is CO2 concentration in surface outflow. IODOX is DO concentration in interflow outflow. IOCO2 is CO2 concentration in interflow outflow. AODOX is DO concentration in active groundwater outflow. AOCO2 is CO2 concentration in active groundwater outflow.
PERLND -- Section PQUAL Input
364
4.4(1).8 PERLND BLOCK -- Section PQUAL input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type NQUALS] [Table-type PQL-AD-FLAGS] --- Table-type QUAL-PROPS | [Table-type QUAL-INPUT] | [Table-type MON-POTFW] | [Table-type MON-POTFS] | repeat for each [Table-type MON-ACCUM] | quality constituent [Table-type MON-SQOLIM] | [Table-type MON-IFLW-CONC] | [Table-type MON-GRND-CONC] | --- ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Tables enclosed in brackets [] are not always required; for example, because allthe values can be defaulted.
PERLND -- Section PQUAL Input
365
4.4(1).8.1 Table-type NQUALS -- Total number of quality constituents simulated ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** NQUALS <-range><nql> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END NQUALS ******* Example ******* NQUALS <PLS > *** # - #NQUAL*** 1 7 8 END NQUALS ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<nql> NQUAL I5 1 1 10---------------------------------------------------------- Explanation The total number of quality constituents simulated in Section PQUAL is indicatedin this table. The set of tables below Table-type PQL-AD-FLAGS is repeated foreach quality constituent.
PERLND -- Section PQUAL Input
366
4.4(1).8.2 Table-type PQL-AD-FLAGS -- Atmospheric deposition flags for PQUAL
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PQL-AD-FLAGS <-range> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PQL-AD-FLAGS ******* Example ******* PQL-AD-FLAGS <PLS > Atmospheric deposition flags *** *** QUAL1 QUAL2 QUAL3 QUAL4 QUAL5 QUAL6 QUAL7 QUAL8 QUAL9 QAL10 #*** # <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 13 -1 0 0 0 11 0 -1 0 0 -1 0 END PQL-AD-FLAGS ******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> PQADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation PQADFG is an array of flags indicating the source of atmospheric deposition data.The QUAL ID number is determined by the order in which the QUALS are input in thetables below. Each QUAL has two flags. The first is for dry or total depositionflux, and the second is for wet deposition concentration. The flag valuesindicate: 0 No deposition of this type is simulated -1 Deposition of this type is input as time series PQADFX or PQADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number. refer to the MONTH-DATA Block (Section 4.11)
It is an error to specify a non-zero flag value for a non-QUALOF.
PERLND -- Section PQUAL Input
367
4.4(1).8.3 Table-type QUAL-PROPS -- Identifiers and flags for a quality constituent
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** QUAL-PROPS <-range><-qualid---> <qt><------------------flags--------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END QUAL-PROPS ******* Example ******* QUAL-PROPS <PLS > Identifiers and Flags*** # - #*** qualid QTID QSD VPFW VPFS QSO VQO QIFW VIQC QAGW VAQC 1 7 BOD kg 0 0 0 1 1 1 0 1 1 END QUAL-PROPS ********************************************************************************
Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<qualid> QUALID 3A4 none none none<qt> QTYID A4 none none none<flags> QSDFG 9I5 0 0 1 VPFWFG 0 0 2 VPFSFG 0 0 1 QSOFG 0 0 1 VQOFG 0 0 1 QIFWFG 0 0 1 VIQCFG 0 0 4 QAGWFG 0 0 1 VAQCFG 0 0 4 ----------------------------------------------------------
PERLND -- Section PQUAL Input
368
Explanation QUALID is a string of up to 10 characters which identifies the quality constituent.
QTYID is a string of up to 4 characters which identifies the units associated withthis constituent (e.g., kg, or lb). These are the units referred to as "qty" insubsequent tables (e.g., Table-type QUAL-INPUT). If QSDFG is 1 then:
1. This constituent is a QUALSD; it is assumed to be sediment-associated. 2. If VPFWFG is 1 or greater, the washoff potency factor may vary throughout
the year. Table-type MON-POTFW is expected. If VPFWFG is 2, the dailyfactors are not computed by interpolation between the monthly values.
3. If VPFSFG is 1, the scour potency factor may vary throughout the year.Table-type MON-POTFS is expected.
If QSOFG is 1 then:
1. This constituent is a QUALOF; it is assumed to be directly associated withoverland flow.
2. If VQOFG is 1 then the rate of accumulation and the limiting storage of theQUALOF may vary throughout the year. Table-types MON-ACCUM and MON-SQOLIMare expected for this QUAL.
If QIFWFG is 1 then:
1. This constituent is a QUALIF; it is assumed to be associated with interflow.2. If VIQCFG 1 or greater, the concentration of this constituent in interflow
outflow may vary throughout the year. Table-type MON-IFLW-CONC is expected.If VIQCFG is 2 or 4, the daily values are obtained directly from the monthlyvalues; no interpolation between monthly values is performed. If VIQCFG is3 or 4, the units of the input concentrations are mg/l; note: this optionrequires that the "qty" units be pounds (English system) or kilograms(Metric system).
If QAGWFG is 1 then:1. This constituent is a QUALGW (groundwater associated).2. If VAQCFG is 1 or greater, the concentration of this constituent in
groundwater outflow may vary throughout the year. Table-type MON-GRND-CONCis expected. If VAQCFG is 2 or 4, the daily values are obtained directlyfrom the monthly values; no interpolation between monthly values isperformed. If VAQCFG is 3 or 4, the units of the input concentrations aremg/l; note: this option requires that the "qty" units be pounds (Englishsystem) or kilograms (Metric system).
PERLND -- Section PQUAL Input
369
4.4(1).8.4 Table-type QUAL-INPUT -- Nonseasonal PQUAL parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** QUAL-INPUT <-range><---------------qual-input-------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END QUAL-INPUT ******* Example ******* QUAL-INPUT <PLS > Storage on surface and nonseasonal parameters*** # - # SQO POTFW POTFS ACQOP SQOLIM WSQOP IOQC AOQC*** 1 7 1.21 17.2 1.1 0.02 2.0 1.70 15.2 17.1 END QUAL-INPUT ********************************************************************************
PERLND -- Section PQUAL Input
370
Details
--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<qual-input> SQO 8F8.0 0.0 0.0 none qty/ac Engl 0.0 0.0 none qty/ha Metric POTFW 0.0 0.0 none qty/ton Engl 0.0 0.0 none qty/tonne Metric POTFS 0.0 0.0 none qty/ton Engl 0.0 0.0 none qty/tonne Metric ACQOP 0.0 0.0 none qty/ac/day Engl 0.0 0.0 none qty/ha/day Metric SQOLIM .000001 .000001 none qty/ac Engl .000002 .000002 none qty/ha Metric WSQOP 1.64 0.01 none in/hr Engl 41.7 0.25 none mm/hr Metric IOQC 0.0 0.0 none qty/ft3 Engl 0.0 0.0 none qty/l Metric AOQC 0.0 0.0 none qty/ft3 Engl 0.0 0.0 none qty/l Metric--------------------------------------------------------------------------------
Explanation
The following variables are applicable only if the constituent is a QUALSD: 1. POTFW is the washoff potency factor.2. POTFS is the scour potency factor.
A potency factor is the ratio of constituent yield to sediment (washoff or scour)outflow.
The following variables are applicable only if the constituent is a QUALOF:1. SQO is the initial storage of QUALOF on the surface of the PLS. 2. ACQOP is the rate of accumulation of QUALOF.3. SQOLIM is the maximum storage of QUALOF.4. WSQOP is the rate of surface runoff which will remove 90 percent of stored
QUALOF per hour.
IOQC is the concentration of the constituent in interflow outflow; it is meaningfulonly if this QUAL is a QUALIF. AOQC is the concentration of the constituent inactive groundwater outflow; it is meaningful only if this QUAL is a QUALGW.
If monthly values are being supplied for any of these quantities, the value in thistable is not relevant; instead, the system expects and uses values supplied inTable-type MON-xxx.
PERLND -- Section PQUAL Input
371
4.4(1).8.5 Table-type MON-POTFW -- Monthly washoff potency factor ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-POTFW <-range><--------------mon-potfw-----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-POTFW ******* Example ******* MON-POTFW <PLS > Value at start of each month for washoff potency factor (lb/ton)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 1.2 2.4 3.6 5.8 10.2 20.2 25.2 30.8 40.2 10.1 2.5 1.7 END MON-POTFW ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-potfw> POTFWM(12) 12F5.0 0.0 0.0 none qty/ton Engl 0.0 0.0 none qty Metric /tonne-------------------------------------------------------------------------------- Explanation This table is only required if VPFWFG is greater than 0 in Table-type QUAL-PROPS.
If VPFWFG is 1 or 3, the input monthly values apply to the first day of the month,and values for intermediate days are obtained by interpolating between successivemonthly values. If VPFWFG is 2 or 4, the input monthly values apply directly toall days of the month.
PERLND -- Section PQUAL Input
372
4.4(1).8.6 Table-type MON-POTFS -- Monthly scour potency factor ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-POTFS <-range><--------------mon-potfs-----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-POTFS ******* Example ******* MON-POTFS <PLS > Value at start of each month for scour potency factor (lb/ton)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 0.9 0.9 0.9 0.8 0.8 1.1 1.1 1.3 1.3 1.0 0.9 0.9 END MON-POTFS ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-potfs> POTFSM(12) 12F5.0 0.0 0.0 none qty/ton Engl 0.0 0.0 none qty Metric /tonne-------------------------------------------------------------------------------- Explanation This table is only required if VPFSFG is 1 in Table-type QUAL-PROPS.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PQUAL Input
373
4.4(1).8.7 Table-type MON-ACCUM -- Monthly accumulation rates of QUALOF ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-ACCUM <-range><---------------mon-accum----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-ACCUM ******* Example ******* MON-ACCUM <PLS > Value at start of month for accum rate of QUALOF (lb/ac.day)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 0.0 0.0 0.01 0.02 0.02 0.04 0.05 0.04 0.02 0.01 0.0 0.0 END MON-ACCUM ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-accum> ACQOPM(12) 12F5.0 0.0 0.0 none qty/ac/day Engl 0.0 0.0 none qty/ha/day Metric-------------------------------------------------------------------------------- Explanation This table is only required if VQOFG is 1 in Table-type QUAL-PROPS.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PQUAL Input
374
4.4(1).8.8 Table-type MON-SQOLIM -- Monthly limiting storage of QUALOF ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-SQOLIM <-range><-----------------mon-sqolim-------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-SQOLIM ******* Example ******* MON-SQOLIM <PLS > Value at start of month for limiting storage of QUALOF (lb/acre)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 10 12 14 18 20 25 30 26 20 13 10 7 END MON-SQOLIM ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-sqolim> SQOLIM(12) 12F5.0 1.E-6 1.E-6 none qty/ac Engl 2.E-6 2.E-6 none qty/ha Metric-------------------------------------------------------------------------------- Explanation This table is only required if VQOFG is 1 in Table-type QUAL-PROPS.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PQUAL Input
375
4.4(1).8.9 Table-type MON-IFLW-CONC -- Monthly concentration of QUAL in interflow
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-IFLW-CONC <-range><----------------mon-iflw-conc-----------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-IFLW-CONC ******* Example ******* MON-IFLW-CONC <PLS > Conc of QUAL in interflow outflow for each month (lb/ft3)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7.0012.0010.0005 0.0 0.0.0002 .005 .002 .001.0016.0014.0012 END MON-IFLW-CONC ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-iflw-conc>IOQCM(12) 12F5.0 0.0 0.0 none qty/ft3 Engl 0.0 0.0 none qty/l Metric
If VIQCFG = 3 or 4 in Table-type QUAL-PROPS: 0.0 0.0 none mg/l Both-------------------------------------------------------------------------------- Explanation This table is only required if VIQCFG is greater than 0 in Table-type QUAL-PROPS.
If VIQCFG is 1 or 3, the input monthly values apply to the first day of the month,and values for intermediate days are obtained by interpolating between successivemonthly values. If VIQCFG is 2 or 4, the input monthly values apply directly toall days of the month.
PERLND -- Section PQUAL Input
376
4.4(1).8.10 Table-type MON-GRND-CONC -- Monthly concentration of QUAL in active groundwater
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-GRND-CONC <-range><---------------mon-grnd-conc------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-GRND-CONC ******* Example ******* MON-GRND-CONC <PLS > Value at start of month for conc of QUAL in groundwater (lb/ft3)** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7.0013.0014.0012.0012.0012.001 .001 .001 .0011.0012.0012.0013 END MON-GRND-CONC ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-grnd-conc>AOQCM(12) 12F5.0 0.0 0.0 none qty/ft3 Engl 0.0 0.0 none qty/l Metric
If VAQCFG = 3 or 4 in Table-type QUAL-PROPS: 0.0 0.0 none mg/l Both-------------------------------------------------------------------------------- Explanation This table is only required if VAQCFG is greater than 0 in Table-type QUAL-PROPS.
If VAQCFG is 1 or 3, the input monthly values apply to the first day of the month,and values for intermediate days are obtained by interpolating between successivemonthly values. If VAQCFG is 2 or 4, the input monthly values apply directly toall days of the month.
PERLND -- Section MSTLAY Input
377
4.4(1).9 PERLND BLOCK -- Section MSTLAY input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** --- Table-type VUZFG | only if Section Table-type UZSN-LZSN | PWATER is Table-type MON-UZSN if VUZFG= 1 | inactive --- Table-type MST-PARM Table-type MST-TOPSTOR Table-type MST-TOPFLX Table-type MST-SUBSTOR Table-type MST-SUBFLX
******************************************************************************** Explanation The exact format of each of the tables mentioned above, except MON-UZSN, isdetailed in the documentation which follows. MON-UZSN is documented under theinput for Section PWATER (4.4(1).4).
Note that if all the fields in a table have default values, the table can beomitted from the User's Control Input. Then, the defaults will be used. Table-types MST-TOPSTOR through MST-SUBFLX should usually not be supplied. See thedocumentation of those tables for further details.
PERLND -- Section MSTLAY Input
378
4.4(1).9.1 Table-type VUZFG -- Variable upper zone flag ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** VUZFG <-range><vuz> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END VUZFG ******* Example ******* VUZFG <PLS >VUZFG*** # - # *** 1 7 1 END VUZFG ******************************************************************************** Details --------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------- <vuz> VUZFG I5 0 0 1 --------------------------------------------------------- Explanation VUZFG is a flag which indicates whether or not the upper zone nominal storagevaries throughout the year or not. A value of zero means it does not vary, a valueof 1 means it does. If it does vary, the system will expect a table of typeMON-UZSN in the User's Control Input. Note that Table VUZFG is only required if Section PWATER is inactive. If thatsection is active VUZFG would have already been provided in the input for PWATER(Table-type PWAT-PARM1).
PERLND -- Section MSTLAY Input
379
4.4(1).9.2 Table-type UZSN-LZSN -- Values of UZSN, LZSN and initial surface storage ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** UZSN-LZSN <-range><-uzsn-><-lzsn-><-surs-> . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . END UZSN-LZSN ******* Example ******* UZSN-LZSN <PLS > UZSN LZSN SURS *** # - # in in in *** 1 7 1.0 6.0 .02 END UZSN-LZSN ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<uzsn> UZSN F8.0 none 0.01 10.0 in Engl none 0.25 250. mm Metric<lzsn> LZSN F8.0 none 0.01 100. in Engl none 0.25 2500. mm Metric<surs> SURS F8.0 .001 .001 100. in Engl .025 .025 2500. mm Metric--------------------------------------------------------------------------------
PERLND -- Section MSTLAY Input
380
Explanation This table is only required if Section PWATER is inactive; otherwise, the datawould have already been supplied in the input for Section PWATER. UZSN is the nominal upper zone storage. The value supplied here is irrelevant ifVUZFG has been set to 1; in that case monthly values for UZSN are supplied inTable-type MON-UZSN.
LZSN is the nominal lower zone storage.
SURS is the initial surface detention storage.
PERLND -- Section MSTLAY Input
381
4.4(1).9.3 Table-type MST-PARM -- Factors used to adjust solute leaching rates ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MST-PARM <-range><--------leach-parms---------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END MST-PARM ******* Example ******* MST-PARM <PLS > SLMPF ULPF LLPF*** # - # *** 1 7 0.5 2.0 2.0 END MST-PARM ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<leach-parms> SLMPF 3F10.0 1.0 .001 1.0 none Both ULPF 1.0 1.0 10.0 none Both LLPF 1.0 1.0 10.0 none Both--------------------------------------------------------------------------------
Explanation
These are the factors that are used to adjust solute percolation rates. SLMPFaffects percolation from the surface layer storage to the upper layer principalstorage. ULPF affects percolation from the upper layer principal storage to thelower layer storage. LLPF affects percolation from the lower layer storage to theactive and inactive groundwater.
PERLND -- Section MSTLAY Input
382
4.4(1).9.4 Table-type MST-TOPSTOR -- Initial moisture storage in each topsoil layer
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MST-TOPSTOR <-range><---------topstor------------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END MST-TOPSTOR Example ******* MST-TOPSTOR <PLS > Topsoil storages (lb/ac)*** # - # SMSTM UMSTM IMSTM*** 1 7 100000 400000 300000 END MST-TOPSTOR ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<topstor> SMSTM 3F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric UMSTM 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric IMSTM 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric-------------------------------------------------------------------------------- Explanation This table is used to specify the initial moisture content in the surface, upperprincipal and upper transitory (interflow) storages, respectively.
Note that the values given in this table only affect the water storages for thestart of the first interval in the run; there is no carry-over of the values beyondthe starting instant. Therefore, in most runs, this table need not be supplied;the default zero values will not cause any problems.
PERLND -- Section MSTLAY Input
383
4.4(1).9.5 Table-type MST-TOPFLX -- Initial fractional fluxes in topsoil layers ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MST-TOPFLX <-range><-------------------top-flux---------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MST-TOPFLX ******* Example ******* MST-TOPFLX <PLS > Fractional fluxes in topsoil layers (/ivl) *** # - # FSO FSP FII FUP FIO*** 1 7 .07 .03 END MST-TOPFLX ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<top-flux> FSO,FSP,FII, 5F10.0 0.0 0.0 1.0 /ivl Both FUP,FIO-------------------------------------------------------------------------------- Explanation These are the initial values of the fractional fluxes of soluble chemicals throughthe topsoil layers of a PLS. Note that the values supplied in this table apply at the instant that the runstarts. The program computes new values each time step and there is no carry-overof values from one time step to the next. Therefore, in most runs, you can omitthis table; the default zero values will not cause any problems.
PERLND -- Section MSTLAY Input
384
4.4(1).9.6 Table-type MST-SUBSTOR -- Initial moisture storage in subsurface layers ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MST-SUBSTOR <-range><-----substor------> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END MST-SUBSTOR ******* Example ******* MST-SUBSTOR <PLS >Subsoil moisture (kg/ha)*** # - # LMSTM AMSTM *** 1 7 800000 1000000 END MST-SUBSTOR ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<substor> LMSTM,AMSTM 2F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric-------------------------------------------------------------------------------- Explanation These are the initial moisture storages in the lower layer and active groundwaterlayers, respectively. Usually, this table should be omitted and the default values used. The commentsmade on this subject in the explanation for Table-type MST-TOPSTOR are alsoapplicable here.
PERLND -- Section MSTLAY Input
385
4.4(1).9.7 Table-type MST-SUBFLX -- Initial fractional fluxes in subsurface layers ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MST-SUBFLX <-range><----------subflux-----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END MST-SUBFLX ******* Example ******* MST-SUBFLX <PLS >Subsurface fractional fluxes (/ivl) *** # - # FLP FLDP FAO *** 1 7 0.1 0.05 END MST-SUBFLX ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<subflux> FLP,FLDP,FAO 3F10.0 0.0 0.0 1.0 /ivl Both-------------------------------------------------------------------------------- Explanation These are the initial fractional fluxes of soluble chemicals through the subsoillayers. Usually, this table should be omitted and the default values taken. The commentson this subject in the explanation for Table-type MST-TOPFLX are applicable here.
PERLND -- Section PEST Input
386
4.4(1).10 PERLND BLOCK -- Section PEST input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
[Table-type PEST-FLAGS] [Table-type PEST-AD-FLAGS] Table-type SOIL-DATA ------------------ Table-type PEST-ID | --- | Table-type PEST-THETA | | Table-type PEST-FIRSTPM for surface layer | if | Table-type PEST-FIRSTPM for upper layer | ADOPFG | Table-type PEST-FIRSTPM for lower layer | =1 | Table-type PEST-FIRSTPM for groundwater layer | | --- | --- | Table-type PEST-CMAX | | Table-type PEST-SVALPM for surface layer | if | Table-type PEST-SVALPM for upper layer | ADOPFG | Table-type PEST-SVALPM for lower layer | =2 | Table-type PEST-SVALPM for groundwater layer | | --- | repeat for --- | each Table-type PEST-CMAX | | pesticide Table-type PEST-NONSVPM for surface layer | if | Table-type PEST-NONSVPM for upper layer | ADOPFG | Table-type PEST-NONSVPM for lower layer | =3 | Table-type PEST NONSVPM for groundwater layer | | --- | Table-type PEST-DEGRAD | | Table-type PEST-STOR1 for surface layer storage | Table-type PEST-STOR1 for upper layer principal storage | Table-type PEST-STOR2 for upper layer transitory storage | | Table-type PEST-STOR1 for lower layer storage | Table-type PEST-STOR1 for groundwater layer storage | ------------------ ********************************************************************************
PERLND -- Section PEST Input
387
Explanation
The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
The comments given alongside the table names above indicate under whatcircumstances a table is expected. Note that if all the fields in a table have default values, the table can beomitted from the User's Control Input. Then, the defaults will be adopted.However, any tables that are repeated for multiple soil layers should generally notbe omitted because the "nth" occurrence of one of these tables refers to thecorresponding "nth" layer. If a table for layer i is omitted, the next occurrenceof the table (intended for layer i+1) will be applied to layer i, and unintendedresults may occur. ADOPFG is the adsorption/desorption option flag. It is described in thedocumentation for Table-type PEST-FLAGS (Sect. 4.4(1).10.1) below.
4.4(1).10.1 Table-type PEST-FLAGS -- Flags for pesticide simulation ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PEST-FLAGS <-range><nps><----itmax----><----adopt----> . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . END PEST-FLAGS ******* Example ******* PEST-FLAGS <PLS > NPST|Max iterations|Adsorp option *** # - # |Pst1 Pst2 Pst3|Pst1 Pst2 Pst3*** 1 7 2 20 20 1 3 END PEST-FLAGS ********************************************************************************
PERLND -- Section PEST Input
388
Details
--------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)--------------------------------------------------------- <nps> NPST I5 1 1 3 <itmax> ITMXPS(*) 3I5 30 1 100 <adopt> ADOPFG(*) 3I5 2 1 3 --------------------------------------------------------- Explanation NPST is the number of pesticides being simulated in the PERLND. NPST is limitedto 3.
ITMXPS is the maximum number of iterations that will be made in trying to solve foradsorbed and dissolved equilibrium using the Freundlich isotherm. A separate valuemay be supplied for each pesticide being simulated. If the Freundlich method isnot being used, these values have no effect. ADOPFG(*) are flags which indicate which method will be used to simulateadsorption/desorption for each pesticide:
1 - first-order kinetics 2 - single-value Freundlich method 3 - non-single value Freundlich method
PERLND -- Section PEST Input
389
4.4(1).10.2 Table-type PEST-AD-FLAGS -- Atmospheric deposition flags for pesticides ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PEST-AD-FLAGS <-range> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PEST-AD-FLAGS ******* Example ******* PEST-AD-FLAGS <PLS > Atmospheric deposition flags *** *** PESTICIDE #1 PESTICIDE #2 PESTICIDE #3 *** CRYS ADSB SOLN CRYS ADSB SOLN CRYS ADSB SOLN #*** # <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 13 -1 10 0 11 -1 0 0 -1 0 END PEST-AD-FLAGS ******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> PEADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation
PEADFG is an array of flags indicating the source of pesticide atmosphericdeposition data. Each pesticide has three forms: crystalline, adsorbed, andsolution. Each form has two flags. The first is for dry or total deposition flux(<f>), and the second is for wet deposition concentration (<c>). The flag valuesindicate:
0 No deposition of this type is simulated -1 Deposition of this type is input as time series PEADFX or PEADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number. (Refer to Section 4.11 for details)
PERLND -- Section PEST Input
390
4.4(1).10.3 Table-type SOIL-DATA -- Soil layer depths and bulk densities ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SOIL-DATA <-range><------------depths------------><-----------bulkdens-----------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SOIL-DATA ******* Example ******* SOIL-DATA <PLS >| Depths (ins) | Bulk density (lb/ft3) |*** # - #|Surface Upper Lower Groundw|Surface Upper Lower Groundw|*** 1 7 .12 6.0 40.0 80. 80. 120. END SOIL-DATA ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<depths> none 4F8.0 none .001 1000 in Engl none .0025 2500 cm Metric<bulkdens> none 4F8.0 103 50 150 lb/ft3 Engl 1.65 0.80 2.40 g/cm3 Metric--------------------------------------------------------------------------------
Explanation
The first four values are the depths (thicknesses) of the surface, upper, lower andgroundwater layers, respectively; the second group of four values are thecorresponding bulk densities of the soil in those layers.
The depth and bulk density are multiplied together by the program to obtain themass of soil in each layer. This is used to compute the concentrations of adsorbedchemicals.
PERLND -- Section PEST Input
391
4.4(1).10.4 Table-type PEST-ID -- Name of pesticide
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PEST-ID <-range><------pestid------> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END PEST-ID
******* Example *******
PEST-ID <PLS > Pesticide*** # - # *** 1 7 Atrazine END PEST-ID
********************************************************************************
Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<pestid> PESTID(*) 5A4 none none none----------------------------------------------------------
Explanation
This table specifies the name of the pesticide to which the data in the followingtables apply.
PERLND -- Section PEST Input
392
4.4(1).10.5 Table-type PEST-THETA -- Pesticide first-order reaction temperature correction parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-THETA <-range><------theta-------> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END PEST-THETA
******* Example *******
PEST-THETA <PLS > Temperature parms *** # - # THDSPS THADPS *** 1 7 1.07 END PEST-THETA
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<theta> THDSPS,THADPS 2F10.0 1.05 1.00 2.00 none Both--------------------------------------------------------------------------------
Explanation
These parameters are used to adjust the desorption and adsorption rate parameters(respectively), using a modified Arrhenius equation:
Rate at T = (Rate at 35 deg C) * (theta)**(T-35)
This table is only required if first-order kinetics are used to simulateadsorption/desorption (ADOPFG=1 in Table-type PEST-FLAGS).
PERLND -- Section PEST Input
393
4.4(1).10.6 Table-type PEST-FIRSTPM -- Pesticide first-order parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-FIRSTPM <-range><----firstparm-----> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END PEST-FIRSTPM
******* Example *******
PEST-FIRSTPM <PLS >First-order parms (/day)*** # - # KDSPS KADPS *** 1 7 .07 .04 END PEST-FIRSTPM
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<firstparm> KDSPS,KADPS 2F10.0 0.0 0.0 none /day Both--------------------------------------------------------------------------------
Explanation
KDSPS and KADPS are the desorption and adsorption rates at 35 deg C.
This table is only required if ADOPFG=1 (first-order kinetics) for this pesticide.
PERLND -- Section PEST Input
394
4.4(1).10.7 Table-type PEST-CMAX -- Maximum solubility of pesticide
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-CMAX <-range><--cmax--> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END PEST-CMAX
******* Example *******
PEST-CMAX <PLS > CMAX*** # - # (ppm)*** 1 7 25.0 END PEST-CMAX
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<cmax> CMAX F10.0 0.0 0.0 none ppm Both--------------------------------------------------------------------------------
Explanation
CMAX is the maximum solubility of the pesticide in water.
This table is only required if ADOPFG= 2 or 3 for this pesticide (Freundlich methodof simulating adsorption/desorption).
PERLND -- Section PEST Input
395
4.4(1).10.8 Table-type PEST-SVALPM -- Pesticide parameters for single-value Freundlich method
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PEST-SVALPM <-range><----------svalpm------------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PEST-SVALPM
******* Example ******* PEST-SVALPM <PLS > XFIX K1 N1*** # - # (ppm) *** 1 7 20. 4.0 1.5 END PEST-SVALPM
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<svalpm> XFIX 3F10.0 0.0 0.0 none ppm Both K1 0.0 0.0 none l/kg Both N1 none 1.0 none none Both--------------------------------------------------------------------------------
Explanation
XFIX is the maximum concentration (on the soil) of pesticide which is permanentlyfixed to the soil. K1 and N1 are the coefficient and exponent parameters for theFreundlich adsorption/desorption equation:
X= K1*C**(1/N1) + XFIX
This table is only used if ADOPFG= 2 for this pesticide (single-value Freundlichmethod). Then, the system expects it to appear four times for this pesticide;first, for the surface layer, second for the upper layer, third for the lowerlayer, and fourth for the active groundwater layer.
PERLND -- Section PEST Input
396
4.4(1).10.9 Table-type PEST-NONSVPM -- Pesticide parameters for non-single value Freundlich method
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-NONSVPM <-range><----------------nonsvpm---------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PEST-NONSVPM
******* Example *******
PEST-NONSVPM <PLS > XFIX K1 N1 N2*** # - # (ppm) *** 1 7 15. 5.0 1.5 1.7 END PEST-NONSVPM
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<nonsvpm> XFIX 4F10.0 0.0 0.0 none ppm Both K1 0.0 0.0 none l/kg Both N1 none 1.0 none none Both N2 none 1.0 none none Both--------------------------------------------------------------------------------
Explanation
XFIX is the maximum concentration (on the soil) of pesticide which is permanentlyfixed in the soil. K1 and N1 are the coefficient and exponent parameters for theFreundlich curve used for adsorption. N2 is the exponent parameter for theauxiliary ("desorption") curve.
This table is only used if ADOPFG= 3 for this pesticide (non-single valueFreundlich method).
PERLND -- Section PEST Input
397
4.4(1).10.10 Table-type PEST-DEGRAD -- Pesticide degradation rates
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-DEGRAD <-range><----------------degrad----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PEST-DEGRAD
******* Example *******
PEST-DEGRAD <PLS > Pesticide degradation rates (/day) *** # - # Surface Upper Lower Groundw*** 1 7 .05 .02 .01 END PEST-DEGRAD
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<degrad> SDGCON,UDGCON, 4F10.0 0.0 0.0 1.0 /day Both LDGCON,ADGCON--------------------------------------------------------------------------------
Explanation
These are the degradation rates of the pesticide in the surface, upper, lower andgroundwater layers, respectively. These rates are not adjusted for temperature.
PERLND -- Section PEST Input
398
4.4(1).10.11 Table-type PEST-STOR1 -- Initial pesticide storage in surface, upper, lower or groundwater layer
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-STOR1 <-range><-cryst--><---ads--><--soln--> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PEST-STOR1
******* Example *******
PEST-STOR1 <PLS >Initial pesticide in surface layer (lb/ac)*** # - # Cryst Ads Soln *** 1 7 10.0 25.0 50.0 END PEST-STOR1
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<cryst>,<ads>, PSCY,PSAD, 3F10.0 0.0 0.0 none lb/ac Engl<soln> PSSU 0.0 0.0 none kg/ha Metric--------------------------------------------------------------------------------
Explanation
PSCY is the pesticide in crystalline form, PSAD is the pesticide in adsorbed formand PSSU is the pesticide in solution.
The values given in this table apply to one of the following four soil storages:surface, upper principal, lower or groundwater. The table should appear fourtimes, once for each layer.
PERLND -- Section PEST Input
399
4.4(1).10.12 Table-type PEST-STOR2 -- Initial pesticide stored in upper layer transitory (interflow) storage
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PEST-STOR2 <-range><--ips---> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END PEST-STOR2
******* Example *******
PEST-STOR2 <PLS > Interflow *** # - # storage(kg/ha)*** 1 7 20.0 END PEST-STOR2
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ips> IPS F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric-------------------------------------------------------------------------------- Explanation IPS is the initial storage of pesticide in the upper layer transitory (interflow)storage. Since only dissolved pesticide is modeled in that storage, only one valueis needed (no crystalline or adsorbed material).
PERLND -- Section NITR Input
400
4.4(1).11 PERLND BLOCK -- Section NITR input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
Table-type SOIL-DATA if section PEST is inactive Table-type NIT-FLAGS Table-type NIT-AD-FLAGS Table-type NIT-FSTGEN Table-type NIT-FSTPM for surface layer Table-type NIT-FSTPM for upper layer Table-type NIT-FSTPM for lower layer Table-type NIT-FSTPM for groundwater layer Table-type NIT-ORGPM for surface layer Table-type NIT-ORGPM for upper layer Table-type NIT-ORGPM for lower layer Table-type NIT-ORGPM for groundwater layer Table-type NIT-AMVOLAT ---- if AMVOFG= 1 --- Table-type NIT-CMAX | Table-type NIT-SVALPM for surface layer | Table-type NIT-SVALPM for upper layer | if FORAFG= 1 Table-type NIT-SVALPM for lower layer | Table-type NIT-SVALPM for groundwater layer | --- --- Table-type NIT-UPTAKE ---------------------- if VNUTFG= 0 | --- | Table-type MON-NITUPT for surface layer | if VNUTFG= 1 | if NUPTFG=0 Table-type MON-NITUPT for upper layer | | Table-type MON-NITUPT for lower layer | | Table-type MON-NITUPT for groundwater layer | | --- --- --- Table-type SOIL-DATA2 | Table-type CROP-DATES | | Table-type NIT-YIELD | Table-type MON-NUPT-FR1 | if NUPTFG= 1 Table-type MON-NUPT-FR2 for surface layer | Table-type MON-NUPT-FR2 for upper layer | Table-type MON-NUPT-FR2 for lower layer | Table-type MON-NUPT-FR2 for groundwater layer | ---
PERLND -- Section NITR Input
401
--- Table-type NIT-UPIMCSAT | | Table-type NIT-UPIMKMAX ---- if VNUTFG= 0 | --- | Table-type MON-NITUPNI | | Table-type MON-NITUPAM | if VNUTFG= 1 | if NUPTFG= 2 or -2 Table-type MON-NITIMNI | | Table-type MON-NITIMAM | | --- --- Note: The preceding group of tables each repeat four times, once for each soil layer, if NUPTFG= 2, but appear only once for all soil layers if NUPTFG= -2 --- Table-type NIT-BGPLRET | if VPRNFG= 0 --- --- Table-type MON-NPRETBG for surface layer | Table-type MON-NPRETBG for upper layer | Table-type MON-NPRETBG for lower layer | if VPRNFG=1 Table-type MON-NPRETBG for groundwater layer | Table-type MON-NPRETFBG | --- --- Table-type NIT-AGUTF -------------------------- if VNUTFG= 0 | --- | Table-type MON-NITAGUTF for surface layer | | Table-type MON-NITAGUTF for upper layer | | Table-type MON-NITAGUTF for lower layer | if VNUTFG= 1 | Table-type MON-NITAGUTF for groundwater layer | | if ALPNFG=1 --- | | Table-type NIT-AGPLRET ------------------------- if VPRNFG= 0 | --- | Table-type MON-NPRETAG | | Table-type MON-NPRETLI for surface layer | | Table-type MON-NPRETLI for upper layer | if VPRNFG= 1 | Table-type MON-NPRETFLI | | --- --- Table-type NIT-STOR1 for surface layer storage Table-type NIT-STOR1 for upper layer principal storage Table-type NIT-STOR2 for upper layer transitory storage, above ground plant and litter storage Table-type NIT-STOR1 for lower layer storage Table-type NIT-STOR1 for groundwater layer storage
********************************************************************************
PERLND -- Section NITR Input
402
Explanation
The exact format of each of the tables mentioned above, except SOIL-DATA, isdetailed in the documentation which follows. SOIL-DATA is documented under theinput for Section PEST (4.4(1).10).
This section is complex, and has many possible tables. Users are cautioned tocarefully observe the options selected and the tables that are required. Thecomments given alongside the table names in the above list indicate under whatcircumstances a table is expected. The flags that determine the expected/requiredtables are described below as well as under the table where they are input(Table-type NIT-FLAGS in Sect. 4.4(1).11.1, below).
AMVOFG is the ammonia volatilization flag.FORAFG is the ammonium adsorption/desorption method flag.VNUTFG is the variable nitrogen plant uptake flag.NUPTFG is the plant uptake method flag.ALPNFG is the "above-ground plant N and litter compartment" flag.VPRNFG is the variable plant return flag.
Note that if all the fields in a table have default values, the table can beomitted from the User's Control Input. Then, the defaults will be adopted.However, any tables that are repeated for multiple soil layers should generallynot be omitted because the "nth" occurrence of one of these tables refers to thecorresponding "nth" layer. If a table for layer i is omitted, the next occurrenceof the table (intended for layer i+1) will be applied to layer i, and unintendedresults may occur.
PERLND -- Section NITR Input
403
4.4(1).11.1 Table-type NIT-FLAGS -- Flags for nitrogen simulation
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-FLAGS <-range><------nitflags---------> . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . END NIT-FLAGS
*******Example*******
NIT-FLAGS <PLS > Nitrogen flags *** x - x VNUT FORA ITMX BNUM CNUM NUPT FIXN AMVO ALPN VNPR *** 1 7 1 3 1 2 1 1 END NIT-FLAGS
********************************************************************************Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<nitflags> VNUTFG 10I5 0 0 1 FORAFG 0 0 1 ITMAXA 30 1 100 BNUMN none 1 1000 CNUMN none 1 1000 NUPTFG 0 -2 2 FIXNFG 0 0 1 AMVOFG 0 0 1 ALPNFG 0 0 1 VNPRFG 0 0 1----------------------------------------------------------
PERLND -- Section NITR Input
404
Explanation
If VNUTFG = 1, the first-order plant uptake parameters for nitrogen are allowedto vary throughout the year and four tables of type MON-NITUPT (or MON-NITUPNI andMON-NITUPAM if saturation kinetics are being simulated) are expected in the User'sControl Input. The first appearance is for the surface layer, 2nd for upperlayer, 3rd for the lower layer, and 4th for the groundwater layer. If VNUTFG = 0,the uptake rates do not vary through the year and a value for each layer isspecified in a single table (Table-type NIT-UPTAKE if first-order kinetics arebeing simulated or NIT-UPIMKMAX if saturation kinetics are being simulated).
FORAFG indicates which method is to be used to simulate adsorption and desorptionof ammonium: 0 first-order kinetics 1 single-value Freundlich method
ITMAXA is the maximum number of iterations that will be attempted in solving theFreundlich equation; applicable only if FORAFG= 1.
BNUMN is the number of time steps that will elapse between recalculation ofbiochemical reaction fluxes. For example, if BNUMN = 10 and the simulation timestep is 5 minutes, then these fluxes will be recalculated every 50 minutes. Allreactions except adsorption/desorption fall into this category. CNUMN is thecorresponding number for the chemical (adsorption/desorption) reactions.
NUPTFG indicated which method is to be used to simulate plant uptake of nitrogen: 0 first-order kinetics 1 yield-based algorithm 2 saturation (Michaelis-Menton) kinetics -2 same as for 2, but with parameters constant over all soil layers
If FIXNFG is 1, nitrogen fixation is simulated. For this option, NUPTFG must alsobe 1. If FIXNFG is zero, or if NUPTFG is not 1, then N fixation is turned off.
If AMVOFG is 1, ammonia volatilization is simulated.
If ALPNFG is 1, above-ground and litter compartments for plant nitrogen aresimulated.
If VNPRFG is 1, then the parameters for describing the return of plant nitrogento the soil are allowed to vary monthly.
PERLND -- Section NITR Input
405
4.4(1).11.2 Table-type NIT-AD-FLAGS -- Atmospheric Deposition Flags for Nitrogen Species
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-AD-FLAGS <-range> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-AD-FLAGS
*******Example*******
NIT-AD-FLAGS <PLS > Atmospheric deposition flags *** *** NITRATE AMMONIA ORGANIC N *** SURF UPPR SURF UPPR SURF UPPR #*** # <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 13 -1 10 0 11 END NIT-AD-FLAGS************************************************* *******************************
Details------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> NIADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation
NIADFG is an array of flags indicating the source of atmospheric deposition data.Each species can be deposited into either the surface or upper soil layers. Eachspecies/layer combination has two flags. The first is for dry or total depositionflux, and the second is for wet deposition concentration. The flag valuesindicate:
0 No deposition of this type is simulated -1 Deposition of this type is input as time series NIADFX or NIADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number. (Refer to Section 4.11 for details)
PERLND -- Section NITR Input
406
4.4(1).11.3 Table-type NIT-FSTGEN -- Nitrogen first-order general parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-FSTGEN <-range><upt-fact><------------temp-parms----------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-FSTGEN
*******Example*******
NIT-FSTGEN <PLS > Upt-facts<--------- Temp-parms (theta) ---------> *** # - # NO3 NH4 PLN KDSA KADA KIMN KAM KDNI KNI KIMA *** 1 7 .5 .5 1.07 1.08 END NIT-FSTGEN
********************************************************************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<upt-fact> NO3UTF 2F5.0 1.0 0.001 1.0 none Both NH4UTF 0.0 0.0 1.0 none Both
<temp-parms> THPLN 8F5.0 1.07 1.0 2.0 none Both THKDSA 1.05 1.0 2.0 none Both THKADA 1.05 1.0 2.0 none Both THKIMN 1.07 1.0 2.0 none Both THKAM 1.07 1.0 2.0 none Both THKDNI 1.07 1.0 2.0 none Both THKNI 1.05 1.0 2.0 none Both THKIMA 1.07 1.0 2.0 none Both--------------------------------------------------------------------------------
PERLND -- Section NITR Input
407
Explanation
These general parameters apply to nitrogen reactions in all the layers; thus, thistable only appears once (or not at all, if defaults are used).
NO3UTF and NH4UTF designate which fraction of nitrogen uptake comes from nitrateand ammonium, respectively. Their sum must be unity; otherwise an error messageis generated. They are used only if first-order or yield-based plant uptake isbeing used (NUPTFG = 0 or 1 in Table-type NIT-FLAGS).
The remaining fields specify the temperature coefficients (theta) for the variousreactions: THPLN Plant uptake (not relevant if NUPTFG = 1) THKDSA Ammonium desorption (only relevant if FORAFG = 0) THKADA Ammonium adsorption (only relevant if FORAFG = 0) THKIMN Nitrate immobilization THKAM Organic N ammonification THKDNI NO3 denitrification THKNI Nitrification THKIMA Ammonium immobilization
4.4(1).11.4 Table-type NIT-FSTPM -- Nitrogen first-order reaction parameters for the surface, upper, lower or active groundwater layer
******************************************************************************** 1 2 3 4 5 6 7 81234567890123456789012345678901234567890123456789 0123456789012345678901234567890********************************************************************************Layout******
NIT-FSTPM <-range><----------------------fstparms--------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-FSTPM
*******Example*******
NIT-FSTPM <PLS >*** Nitrogen first-order parameters for lower layer (/day) # - #*** KDSAM KADAM KIMNI KAM KDNI KNI KIMAM 1 7 .05 .03 .02 .05 END NIT-FSTPM
********************************************************************************
PERLND -- Section NITR Input
408
Details
--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<fstparms> KDSAM,KADAM, 7F10.0 0.0 0.0 none /day Both KIMNI,KAM,KDNI, KNI,KIMAM----------------------------------------------------------- ---------------------
Explanation
These are the first-order reaction rate parameters for a layer of soil: KDSAM Ammonium desorption (only relevant if FORAFG = 0) KADAM Ammonium adsorption (only relevant if FORAFG = 0) KIMNI Nitrate immobilization (only relevant if NUPTFG = 0 or 1) KAM Organic N ammonification KDNI Denitrification of NO3 KNI Nitrification KIMAM Ammonium immobilization (only relevant if NUPTFG = 0 or 1)
HSPF expects this table to appear four times in the User's Control Input; firstfor the surface layer, second for the upper layer, third for the lower layer, andfourth for the active groundwater layer. If one or more occurrences of the tableare missing, all reaction parameters for the affected layer(s) will be defaultedto zero.
PERLND -- Section NITR Input
409
4.4(1).11.5 Table-type NIT-ORGPM -- Organic nitrogen transformation parameters for the surface, upper, lower, or groundwater layer
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-ORGPM <-range><------------------orgpm---------------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END NIT-ORGPM
*******Example*******
NIT-ORGPM <PLS > KLON KRON KONLR THNLR *** # - # /day *** 1 3 250. 200. .02 1.07 END NIT-ORGPM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<orgpm> KLON 4F10.0 1.0E20 0.0 1.0E20 none Both KRON 1.0E20 0.0 1.0E20 none Both KONLR 0.0 0.0 none /day Both THNLR 1.07 1.0 2.0 none Both--------------------------------------------------------------------------------
Explanation
This table is only required in order to simulate detailed organic nitrogentransformations and transport (designed primarily for forests). The table issupplied four times - once for each soil layer. If one or more occurrences of thetable are missing, all parameters for the affected layer(s) will be defaulted.
KLON is the particulate/soluble partitioning coefficient for labile organic N.KRON is the particulate/soluble partitioning coefficient for refractory organicN. KONLR is the first-order conversion rate of labile to refractory particulateorganic N and THNLR is the associated temperature correction coefficient.
PERLND -- Section NITR Input
410
4.4(1).11.6 Table-type NIT-AMVOLAT -- Ammonia volatilization parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-AMVOLAT <-range><-------------------------amvopm---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-AMVOLAT
*******Example*******
NIT-AMVOLAT <PLS > SKVOL UKVOL LKVOL AKVOL THVOL TRFVOL *** x - x (/day) (/day) (/day) (/day) (-) (deg C) *** 1 3 0.4 0.2 0.1 0.0 1.07 20.0 END NIT-AMVOLAT
********************************************************************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<amvopm> SKVOL,UKVOL, 6F10.0 0.0 0.0 none /day Both LKVOL,AKVOL THVOL 1.07 1.0 2.0 none Both TRFVOL 20.0 0.0 35.0 deg C Both--------------------------------------- -----------------------------------------
Explanation
SKVOL, UKVOL, LKVOL, and AKVOL are the ammonia volatilization rates in thesurface, upper, lower, and groundwater layers, respectively. THVOL is thetemperature correction coefficient. TRFVOL is the reference temperature for thecorrection.
This table is only used if volatilization of ammonia is simulated (AMVOFG = 1 inTable-type NIT-FLAGS).
PERLND -- Section NITR Input
411
4.4(1).11.7 Table-type NIT-CMAX -- Maximum solubility of ammonium
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-CMAX <-range><--cmax--> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END NIT-CMAX
*******Example*******
NIT-CMAX <PLS > CMAX*** # - # (ppm)*** 1 5 15.0 END NIT-CMAX
********************************************************************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<cmax> CMAX F10.0 0.0 0.0 none ppm Both------------------------------------------------- -------------------------------
Explanation
CMAX is the maximum solubility of ammonium in water. This table only appearsonce, and is only required if FORAFG = 1 (adsorption/desorption is simulated usingsingle-value Freundlich method).
PERLND -- Section NITR Input
412
4.4(1).11.8 Table-type NIT-SVALPM -- Nitrogen single value Freundlich adsorption/desorption parameters
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NIT-SVALPM <-range><-----------svalpm-----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END NIT-SVALPM
*******Example*******
NIT-SVALPM <PLS > XFIX K1 N1*** # - # (ppm) *** 1 3 10.0 5.0 1.2 END NIT-SVALPM
********************************************************************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<svalpm> XFIX 3F10.0 0.0 0.0 none ppm Both K1 0.0 0.0 none l/kg Both N1 none 1.0 none none Both--------------------------------------------------------------------------------
Explanation
This table is only required if FORAFG = 1; that is, adsorption and desorption ofammonium is simulated using the single-value Freundlich method.
This table is exactly analogous to Table-type PEST-SVALPM.
PERLND -- Section NITR Input
413
4.4(1).11.9 Table-type NIT-UPTAKE -- Nitrogen plant uptake rate parameters
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NIT-UPTAKE <-range><---------------uptake-----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END NIT-UPTAKE
*******Example*******
NIT-UPTAKE <PLS >Nitrogen plant uptake rates (/day) *** # - # Surface Upper Lower Groundw*** 1 2 0.01 0.02 0.01 END NIT-UPTAKE
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<uptake> SKPLN,UKPLN, 4F10.0 0.0 0.0 none /day Both LKPLN,AKPLN------------------------------------------------- -------------------------------
Explanation
SKPLN, UKPLN, LKPLN and AKPLN are the plant nitrogen uptake reaction rateparameters for the surface, upper, lower, and active groundwater layers,respectively. This table is required when first-order plant uptake is being used,and uptake parameters do not vary monthly (NUPTFG = 0 and VNUTFG = 0 in Table-typeNIT-FLAGS).
PERLND -- Section NITR Input
414
4.4(1).11.10 Table-type MON-NITUPT -- Monthly plant uptake parameters for nitrogen, for the surface, upper, lower or groundwater layer
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MON-NITUPT <-range><------------------------mon-uptake------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NITUPT
*******Example*******
MON-NITUPT <PLS > Plant uptake parms for nitrogen in upper layer (/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 4 .01 .03 .05 .05 .03 .01 END MON-NITUPT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-uptake> KPLNM(*) 12F5.0 0.0 0.0 none /day Both--------------------------------------------------------------------------------
Explanation
This table is required if first-order plant uptake is being used and the plantuptake parameters vary throughout the year (NUPTFG = 0 and VNUTFG = 1 inTable-type NIT-FLAGS). The entire table is supplied four times; first for thesurface layer, second for the upper layer, third for the lower layer, and fourthfor the active groundwater layer. If omitted, default values will be supplied.For example, if the third and fourth occurrences of the table are omitted, theparameters for the lower and groundwater layers will default to zero.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
415
4.4(1).11.11 Table-type SOIL-DATA2 -- Wilting points for yield-based plant uptake
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SOIL-DATA2 <-range><----wiltpt----------------------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END SOIL-DATA2
*******Example*******
SOIL-DATA2 <PLS > Wilting points for each soil layer *** # - # SURFACE UPPER LOWER ACT GW *** 1 7 .02 .01 .01 .015 END SOIL-DATA2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<wiltpt> SWILTP,UWILTP 4F10.0 0.0 0.0 none none Both LWILTP,AWILTP------------------------------------------------- -------------------------------
Explanation
The wilting point, which is input as a fraction (volume-basis) is used todetermine when the soil is too dry for plant uptake to occur when the yield-basedmethod of plant uptake is being used (NUPTFG = 1 in Table-type NIT-FLAGS and/orPUPTFG = 1 in Table-type PHOS-FLAGS). This table should only be entered once forthe NITR and PHOS sections.
PERLND -- Section NITR Input
416
4.4(1).11.12 Table-type CROP-DATES -- Planting and harvesting dates for yield- based plant uptake
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CROP-DATES <-range><ncr> <m><d> <m><d> <m><d> <m><d> <m><d> <m><d> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END CROP-DATES
*******Example*******
CROP-DATES <PLS > CROP 1 CROP 2 CROP 3 *** # - # NCRP PM PD HM HD PM PD HM HD PM PD HM HD *** 1 2 4 15 8 20 9 5 9 29 END CROP-DATES
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<ncr> NCRP I5 1 1 3<m> CRPDAT(*) 2I3 1 1 12<d> 1 1 31----------------------------------------------------------
Explanation
NCRP is the number of crops per year.
CRPDAT is the month and day of planting and harvesting for each crop. Cropseasons cannot overlap, but a season may wrap around the end of the calendar year.
Cropping dates are required only when the yield-based method of plant uptake isbeing used (NUPTFG = 1 in Table-type NIT-FLAGS and/or PUPTFG = 1 in Table-typePHOS-FLAGS). This table should only be entered once for the NITR and PHOSsections.
PERLND -- Section NITR Input
417
4.4(1).11.13 Table-type NIT-YIELD -- Yield-based nitrogen plant uptake parameters
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NIT-YIELD <-range><-target-><-maxrat-> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END NIT-YIELD
*******Example*******
NIT-YIELD <PLS > NUPTGT NMXRAT *** # - # (LB/AC) *** 1 100.00 1.5 END NIT-YIELD
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<-target-> NUPTGT F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric<-maxrat-> NMXRAT F10.0 1.0 1.0 none none Both--------------------------------------------------------------------------------
Explanation
NUPTGT is the total annual target for plant uptake of nitrogen for all soil layersand all crops during the calendar year.
NMXRAT is the ratio of the maximum uptake rate to the optimum (target) rate whenthe crop is making up a deficit in nitrogen uptake.
This table is required only when yield-based plant uptake is being used (i.e.,NUPTFG = 1 in Table-type NIT-FLAGS).
PERLND -- Section NITR Input
418
4.4(1).11.14 Table-type MON-NUPT-FR1 -- Monthly fractions for yield-based plant uptake of nitrogen
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MON-NUPT-FR1 <-range><------------------------mon-nuptfr------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NUPT-FR1
*******Example*******
MON-NUPT-FR1 <PLS > Monthly fractions for plant uptake target *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 .1 .2 .2 .2 .1 .2 2 .1 .1 .05 .05 .1 .1 .1 .05 .05 .1 .1 .1 END MON-NUPT-FR1
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-nuptfr> NUPTFM(*) 12F5.0 0.0 0.0 1.0 none Both------------------------------------------------- -------------------------------
Explanation
These are the fractions of the total annual nitrogen plant uptake target (NUPTGTin Table-type NIT-YIELD) applied to each month. The fractions across the yearmust sum to unity; otherwise, an error message is generated. This table is onlyrequired when yield-based plant uptake of nitrogen is being used (NUPTFG = 1 inTable-type NIT-FLAGS).
PERLND -- Section NITR Input
419
4.4(1).11.15 Table-type MON-NUPT-FR2 -- Monthly fractions for yield-based plant uptake of nitrogen from a soil layer
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MON-NUPT-FR2 <-range><------------------------mon-layfr-------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NUPT-FR2
*******Example*******
MON-NUPT-FR2 <PLS > Monthly fractions for plant uptake target from surface *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 2 .15 .15 .15 .1 .1 .1 .1 .1 .15 .12 .12 .1 END MON-NUPT-FR2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-layfr> SNUPTM(*),UNUPTM(*) 12F5.0 0.0 0.0 1.0 none Both LNUPTM(*),ANUPTM(*)------------------------------------------------- -------------------------------
Explanation
These are the fractions of the monthly nitrogen plant uptake target (NUPTGT inTable-type NIT-YIELD times NUPTFM in Table-type MON-NUPT-FR1) applied to each soillayer: surface, upper, lower, and active groundwater. The fractions across thefour layers (NOT across the 12 months, as for MON-NUPT-FR1) must sum to unity;otherwise an error message is generated. This table is only required when yield-based plant uptake of nitrogen is being used (NUPTFG = 1 in Table-type NIT-FLAGS).Then, the system expects it to appear four times: first, for the surface layer,second for the upper layer, etc. If one or more occurrences of the table aremissing, all parameters for the affected layer(s) will be defaulted to zero.
PERLND -- Section NITR Input
420
4.4(1).11.16 Table-type NIT-UPIMCSAT -- Half saturation constants for nitrogen uptake and immobilization when using saturation kinetics method (for surface, upper, lower, or groundwater layer)
************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout******
NIT-UPIMCSAT <-range><---------------csatpm-----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END NIT-UPIMCSAT
*******Example*******
NIT-UPIMCSAT <PLS > CSUNI CSUAM CSINI CSIAM *** x - x (ug/l) (ug/l) (ug/l) (ug/l) *** 1 3 40. 15. 4.0 1.5 END NIT-UPIMCSAT
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<csatpm> CSUNI,CSUAM 4F10.0 0.0 0.0 none ug/l Both CSINI,CSIAM--------------------------------------------------------------------------------Explanation
CSUNI and CSUAM are the nitrate and ammonia half saturation constants for uptake.CSINI and CSIAM are the nitrate and ammonia half saturation constants forimmobilization.
This table is only required if nitrogen uptake and immobilization are beingsimulated using the saturation kinetics method (NUPTFG = 2 or -2 in Table-typeNIT-FLAGS). If NUPTFG = 2, HSPF expects this table to appear four times in theUser's Control Input, once for each soil layer. If one or more occurrences of thetable are missing, all parameters for the affected layer(s) will be defaulted tozero. If NUPTFG = -2, HSPF expects one occurrence of this table, and uses thesame parameters for all four soil layers.
PERLND -- Section NITR Input
421
4.4(1).11.17 Table-type NIT-UPIMKMAX -- Maximum rate constants for nitrogen uptake and immobilization when using saturation kinetics method (for surface, upper, lower, or groundwater layer)
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NIT-UPIMKMAX <-range><---------------kmaxpm-----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END NIT-UPIMKMAX
*******Example*******
NIT-UPIMKMAX <PLS > Maximum plant uptake and immobilization rates (mg/l/day) *** x - x KUPNI KUPAM KIMNI KIMAM *** 1 3 1.0 0.6 .05 .02 END NIT-UPIMKMAX
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<kmaxpm> KUPNI,KUPAM 4F10.0 0.0 0.0 none mg/l/day Both KIMNI,KIMAM--------------------------------------------------------------------------------Explanation
KUPNI and KUPAM are the nitrate and ammonia maximum uptake rates. KIMNI and KIMAMare the nitrate and ammonia maximum immobilization rates.
This table is only required if nitrogen uptake and immobilization are beingsimulated using the saturation kinetics method (NUPTFG = 2 or -2 in Table-typeNIT-FLAGS). If NUPTFG = 2, HSPF expects this table to appear four times in theUser's Control Input, once for each soil layer. If one or more occurrences of thetable are missing, all parameters for the affected layer(s) will be defaulted tozero. If NUPTFG = -2, HSPF expects one occurrence of this table, and uses thesame parameters for all four soil layers.
PERLND -- Section NITR Input
422
4.4(1).11.18 Table-type MON-NITUPNI -- Monthly nitrate uptake maximum rates when using saturation kinetics method (for the surface, upper, lower or groundwater layer)
************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout******
MON-NITUPNI <-range><------------------------mon-upnipm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NITUPNI
*******Example*******
MON-NITUPNI <PLS > Maximum plant uptake rate for nitrate (mg/l/day) *** x - x JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .05 .25 .75 1.2 2.0 2.5 2.5 2.5 2.0 1.2 .75 .25 END MON-NITUPNI
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-upnipm> KUNIM(*) 12F5.0 0.0 0.0 none mg/l/day Both------------------------------------------------- -------------------------------Explanation
This table contains the maximum nitrate uptake rates when using the saturationkinetics method. The table is required if saturation kinetics are being simulatedfor uptake and the rates vary monthly (NUPTFG = 2 or -2 and VNUTFG = 1 in Table-type NIT-FLAGS). If NUPTFG = 2, HSPF expects this table to appear four times inthe User's Control Input, once for each soil layer. If one or more occurrencesof the table are missing, all parameters for the affected layer(s) will bedefaulted to zero. If NUPTFG = -2, HSPF expects one occurrence of this table, anduses the same parameters for all four soil layers.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
423
4.4(1).11.19 Table-type MON-NITUPAM -- Monthly ammonia uptake maximum rates when using saturation kinetics method (for the surface, upper, lower or groundwater layer)
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MON-NITUPAM <-range><------------------------mon-upampm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NITUPAM
*******Example*******
MON-NITUPAM <PLS > Max ammonia uptake rate in upper layer (mg/l/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .03 .06 .12 .30 .45 .60 .60 .60 .45 .30 .15 .08 END MON-NITUPAM
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-upampm> KUAMM(*) 12F5.0 0.0 0.0 none mg/l/day Both------------------------------------------------- -------------------------------Explanation
This table contains the maximum ammonia uptake rates when using the saturationkinetics method. The table is required if saturation kinetics are being simulatedfor uptake and the rates vary monthly (NUPTFG = 2 or -2 and VNUTFG = 1 in Table-type NIT-FLAGS). If NUPTFG = 2, HSPF expects this table to appear four times inthe User's Control Input, once for each soil layer. If one or more occurrencesof the table are missing, all parameters for the affected layer(s) will bedefaulted to zero. If NUPTFG = -2, HSPF expects one occurrence of this table, anduses the same parameters for all four soil layers.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
424
4.4(1).11.20 Table-type MON-NITIMNI -- Monthly nitrate immobilization rates when using saturation kinetics method (for the surface, upper, lower or groundwater layer)
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MON-NITIMNI <-range><------------------------mon-imnipm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NITIMNI
*******Example*******
MON-NITIMNI <PLS > Nitrate immobilization rate in upper layer (mg/l/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .01 .01 .02 .02 .03 .04 .04 .04 .03 .03 .02 .01 END MON-NITIMNI
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-imnipm> KINIM(*) 12F5.0 0.0 0.0 none mg/l/day Both------------------------------------------------- -------------------------------Explanation
This table contains the maximum nitrate immobilization rates when using thesaturation kinetics method. The table is required if saturation kinetics arebeing simulated for immobilization, and the rates vary monthly (NUPTFG = 2 or -2and VNUTFG = 1 in Table-type NIT-FLAGS). If NUPTFG = 2, HSPF expects this tableto appear four times in the User's Control Input, once for each soil layer. Ifone or more occurrences of the table are missing, all parameters for the affectedlayer(s) will be defaulted to zero. If NUPTFG = -2, HSPF expects one occurrenceof this table, and uses the same parameters for all four soil layers.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
425
4.4(1).11.21 Table-type MON-NITIMAM -- Monthly ammonia immobilization rates when using saturation kinetics method (for the surface, upper, lower or groundwater layer)
************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout******
MON-NITIMAM <-range><------------------------mon-imampm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NITIMAM
*******Example*******
MON-NITIMAM <PLS > Ammonia immobilization rate in upper layer (mg/l/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .01 .01 .01 .02 .02 .02 .03 .03 .02 .02 .02 .01 END MON-NITIMAM
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-imampm> KIAMM(*) 12F5.0 0.0 0.0 none mg/l/day Both------------------------------------------------- -------------------------------Explanation
This table contains the maximum ammonia immobilization rates when using thesaturation kinetics method. The table is required if saturation kinetics arebeing simulated for immobilization, and the rates vary monthly (NUPTFG = 2 or -2and VNUTFG = 1 in Table-type NIT-FLAGS). If NUPTFG = 2, HSPF expects this tableto appear four times in the User's Control Input, once for each soil layer. Ifone or more occurrences of the table are missing, all parameters for the affectedlayer(s) will be defaulted to zero. If NUPTFG = -2, HSPF expects one occurrenceof this table, and uses the same parameters for all four soil layers.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
426
4.4(1).11.22 Table-type NIT-BGPLRET -- Below-ground plant nitrogen return rates.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-BGPLRET <-range><---------------plrepm---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-BGPLRET
*******Example*******
NIT-BGPLRET Below-ground plant return rates and refractory fraction *** <PLS> SKPRBN UKPRBN LKPRBN AKPRBN BGNPRF *** x - x (/day) (/day) (/day) (/day) *** 1 3 .02 .01 .01 0.0 0.1 END NIT-BGPLRET
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<plrepm> SKPRBN F10.0 0.0 0.0 none /day Both UKPRBN F10.0 0.0 0.0 none /day Both LKPRBN F10.0 0.0 0.0 none /day Both AKPRBN F10.0 0.0 0.0 none /day Both BGNPRF F10.0 0.0 0.0 none none Both--------------------------------------------------------------------------------
Explanation
SKPRBN, UKPRBN, LKPRBN, and AKPRBN are the first-order return rates ofbelow-ground plant N to organic N storage in the four layers. BGNPRF is thefraction of plant N return that becomes particulate refractory organic N. (Therest becomes particulate labile organic N.)
This table is only used when plant return rates are constant (VPRNFG = 0 inTable-type NIT-FLAGS).
PERLND -- Section NITR Input
427
4.4(1).11.23 Table-type MON-NPRETBG -- Monthly below-ground plant N return rates for the surface, upper, lower or groundwater layer
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-NPRETBG <-range><------------------------mon-plrepm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NPRETBG
*******Example*******
MON-NPRETBG <PLS > Return rates for below-ground plant N in upper layer (/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .01 .03 .05 .05 .03 .01 END MON-NPRETBG
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-plrepm> KRBNM(*) 12F5.0 0.0 0.0 none /day Both--------------------------------------------------------------------------------
Explanation
This table contains the first-order return rates of below-ground plant N toorganic N. The table is used if the plant N return parameters vary throughout theyear (VPLRFG = 1 in Table-type NIT-FLAGS). The entire table is supplied fourtimes; first for the surface layer, second for the upper layer, third for thelower layer, and fourth for the active groundwater layer. If omitted, defaultvalues will be supplied. For example, if the third and fourth occurrences of thetable are omitted, the parameters for the lower and groundwater layers willdefault to zero.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
428
4.4(1).11.24 Table-type MON-NPRETFBG -- Monthly refractory fractions for below-ground plant N return
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-NPRETFBG <-range><------------------------mon-plrefr------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NPRETFBG
*******Example*******
MON-NPRETFBG <PLS > Monthly refractory fractions for below-ground plant N return *** x - x JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .02 .02 .03 .04 .04 .05 .05 .05 .04 .04 .03 .03 END MON-NPRETFBG
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-plrefr> BNPRFM(*) 12F5.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
Explanation
This table contains the fractions of below-ground plant N return which becomeparticulate refractory organic N. (The rest becomes particulate labile organicN.) The table is used only if the plant N return parameters vary throughout theyear (VPLRFG = 1 in Table-type NIT-FLAGS).
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
429
4.4(1).11.25 Table-type NIT-AGUTF -- Above-ground plant uptake fractions
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-AGUTF <-range><---------------agutf------------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END NIT-AGUTF
*******Example*******
NIT-AGUTF <PLS > Above-ground plant uptake fractions *** x - x SANUTF UANUTF LANUTF AANUTF *** 1 3 0.8 0.8 0.7 0.7 END NIT-AGUTF
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<agutf> SANUTF,UANUTF, 4F10.0 0.0 0.0 1.0 none Both LANUTF,AANUTF------------------------------------------------- -------------------------------Explanation
SANUTF, UANUTF, LANUTF and AANUTF are the above-ground plant uptake fractions forthe surface, upper, lower, and active groundwater layers, respectively. This tableis used only when the above-ground compartment is being simulated and uptakeparameters do not vary monthly (ALPNFG = 1 and VNUTFG = 0 in Table-typeNIT-FLAGS).
PERLND -- Section NITR Input
430
4.4(1).11.26 Table-type MON-NITAGUTF -- Monthly above-ground plant uptake fractions for nitrogen, for the surface, upper, lower or groundwater layer
************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout******
MON-NITAGUTF <-range><------------------------mon-agutf-------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NITAGUTF
*******Example*******
MON-NITAGUTF <PLS > Monthly above-ground fractions for plant uptake *** x - x JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .70 .70 .70 .75 .75 .80 .80 .80 .75 .75 .70 .70 END MON-NITAGUTF
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-agutf> ANUFM(*) 12F5.0 0.0 0.0 1.0 none Both------------------------------------------------- -------------------------------
Explanation
This table contains the fractions of plant uptake which go to above-ground plantN storage. The table is used only if the above-ground compartment is beingsimulated and the plant uptake parameters vary throughout the year (ALPNFG = 1 andVNUTFG = 1 in Table-type NIT-FLAGS). The table is supplied four times; first forthe surface layer, second for the upper layer, third for the lower layer, andfourth for the active groundwater layer. If omitted, default values will besupplied. For example, if the third and fourth occurrences of the table areomitted, the parameters for the lower and groundwater layers will default to zero.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
431
4.4(1).11.27 Table-type NIT-AGPLRET -- Above-ground plant nitrogen return rates.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-AGPLRET <-range><------------plrepm--------------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END NIT-AGPLRET
*******Example*******
NIT-AGPLRET Above-ground plant return rates and refractory fraction <PLS> AGKPRN SKPRLN UKPRLN LINPRF x - x (/day) (/day) (/day) 1 3 .01 .02 .01 0.1 END NIT-AGPLRET
************************************************* *******************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<plrepm> AGKPRN F10.0 0.0 0.0 none /day Both SKPRLN F10.0 0.0 0.0 none /day Both UKPRLN F10.0 0.0 0.0 none /day Both LINPRF F10.0 0.0 0.0 none none Both--------------------------------------- -----------------------------------------Explanation
AGKPRN is the first-order return rate of above-ground plant N to litter N. SKPRLNand UKPRLN are the first-order return rates of litter N to organic N storage inthe surface and upper soil layers, respectively. LINPRF is the fraction of litterN return that becomes particulate refractory organic N. (The rest becomesparticulate labile organic N.)
This table is only used when the above-ground and litter compartments are beingsimulated for nitrogen, and plant return rates are constant (i.e., ALPNFG = 1 andVPRNFG = 0 in Table-type NIT-FLAGS).
PERLND -- Section NITR Input
432
4.4(1).11.28 Table-type MON-NPRETAG -- Monthly above-ground plant N return rates to litter N
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-NPRETAG <-range><------------------------mon-plrepm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NPRETAG
*******Example*******
MON-NPRETAG <PLS > Return rates for above-ground plant N to litter N (/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .01 .03 .05 .05 .03 .01 END MON-NPRETAG
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-plrepm> KRANM(*) 12F5.0 0.0 0.0 none /day Both--------------------------------------------------------------------------------
Explanation
This table contains the first-order return rate of above-ground plant N to litterN. The table is used only when the above-ground compartment is being simulatedand the plant N return parameters vary throughout the year (i.e., ALPNFG = 1 andVPLRFG = 1 in Table-type NIT-FLAGS).
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
433
4.4(1).11.29 Table-type MON-NPRETLI -- Monthly litter plant N return rates for the surface or upper layer
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-NPRETLI <-range><------------------------mon-plrepm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NPRETLI
*******Example*******
MON-NPRETLI <PLS > Return rates for litter plant N to upper layer (/day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 4 .01 .03 .05 .05 .03 .01 END MON-NPRETLI
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-plrepm> KRLNM(*) 12F5.0 0.0 0.0 none /day Both--------------------------------------------------------------------------------
Explanation
This table contains the return rates of litter plant N to particulate labileorganic N in the surface or upper layer. The table is required if the plant Nreturn parameters vary throughout the year (VPLRFG = 1 in Table-type NIT-FLAGS).The entire table is supplied two times; first for the surface layer and second forthe upper layer. If omitted, default values will be supplied. For example, ifthe second occurrence of the table is omitted, the parameters for the upper layerwill default to zero.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
434
4.4(1).11.30 Table-type MON-NPRETFLI -- Monthly refractory fractions for litter N return
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-NPRETFLI <-range><------------------------mon-plrefr------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-NPRETFLI
*******Example*******
MON-NPRETFLI <PLS > Monthly refractory fractions for litter N return *** x - x JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 4 .02 .02 .03 .04 .04 .05 .05 .05 .04 .04 .03 .03 END MON-NPRETFLI
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-plrefr> LNPRFM(*) 12F5.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
Explanation
This table contains the fractions of litter N return which become particulaterefractory organic N. (The rest becomes particulate labile organic N.) The tableis used only if the litter compartment is being simulated and the plant N returnparameters vary throughout the year (ALPNFG = 1 and VPLRFG = 1 in Table-typeNIT-FLAGS).
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section NITR Input
435
4.4(1).11.31 Table-type NIT-STOR1 -- Initial storage of nitrogen in the surface, upper, lower, or groundwater layer
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-STOR1 <-range><-----------------------nit-stor1--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-STOR1
*******Example*******
NIT-STOR1 <PLS > Initial storage of N (lb/ac) *** x - x LORGN AMAD AMSU NO3 PLTN RORGN *** 1 4 100. 500. 50. END NIT-STOR1
********************************************************************************
Details--------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<nit-stor1> LORGN,AMAD,AMSU, 6F10.0 0.0 0.0 none lb/ac Engl NO3,PLTN,RORGN 0.0 0.0 none kg/ha Metric--------------------------------------------------------------------------------
Explanation
This table is similar in organization to Table-type PEST-STOR1. It specifies theinitial storage of N in one of the four major soil layers. The values in thetable are:
LORGN Labile organic nitrogen AMAD Adsorbed ammonium AMSU Solution ammonium NO3 Nitrate PLTN Nitrogen stored in plants RORGN Refractory organic nitrogen
PERLND -- Section NITR Input
436
4.4(1).11.32 Table-type NIT-STOR2 -- Initial storage of nitrogen in upper layer transitory (interflow) storage
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NIT-STOR2 <-range><-------------------------nit-stor2------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NIT-STOR2
*******Example*******
NIT-STOR2 <PLS > Initial N in interflow, above-ground, and litter storage (lb/ac) *** x - x IAMSU INO3 ISLON ISRON AGPLTN LITTRN *** 1 2 100. 10. END NIT-STOR2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<nit-stor2> IAMSU,INO3,ISLON, 6F10.0 0.0 0.0 none lb/ac Engl ISRON,AGPLTN,LITTRN 0.0 0.0 none kg/ha Metric------------------------------------------------- -------------------------------
Explanation
This table specifies the initial storage of N in the upper layer transitory(interflow) storage. If the above-ground and litter compartments are beingsimulated (ALPNFG = 1 in Table-type NIT-FLAGS), then the initial storage for thesecompartments is also specified.
IAMSU Solution ammonium INO3 Nitrate ISLON Solution labile organic nitrogen ISRON Solution refractory organic nitrogen AGPLTN Above-ground plant nitrogen (only relevant if ALPNFG = 1) LITTRN Litter nitrogen (only relevant if ALPNFG = 1)
PERLND -- Section PHOS Input
437
4.4(1).12 PERLND BLOCK -- Section PHOS input
******************************************************************************** 1 2 3 4 5 6 7 81234567890123456789012345678901234567890123456789 0123456789012345678901234567890********************************************************************************Layout****** Table-type SOIL-DATA if sections PEST and NITR are inactive Table-type PHOS-FLAGS Table-type PHOS-AD-FLAGS Table-type PHOS-FSTGEN Table-type PHOS-FSTPM for surface layer Table-type PHOS-FSTPM for upper layer Table-type PHOS-FSTPM for lower layer Table-type PHOS-FSTPM for groundwater layer --- Table-type PHOS-CMAX | Table-type PHOS-SVALPM for surface layer | if (single value Table-type PHOS-SVALPM for upper layer | FORPFG= Freundlich Table-type PHOS-SVALPM for lower layer | 1 method) Table-type PHOS-SVALPM for groundwater layer | --- --- Table-type PHOS-UPTAKE ---------------------- if VPUTFG= 0 | --- | Table-type MON-PHOSUPT for surface layer | if VPUTFG= 1 | if PUPTFG= 0 Table-type MON-PHOSUPT for upper layer | | Table-type MON-PHOSUPT for lower layer | | Table-type MON-PHOSUPT for groundwater layer | | --- --- --- --- Table-type SOIL-DATA2 | if NITR is inactive | Table-type CROP-DATES | or NUPTFG= 0 | --- | Table-type PHOS-YIELD | Table-type MON-PUPT-FR1 | if PUPTFG= 1 Table-type MON-PUPT-FR2 for surface layer | Table-type MON-PUPT-FR2 for upper layer | Table-type MON-PUPT-FR2 for lower layer | Table-type MON-PUPT-FR2 for groundwater layer | --- Table-type PHOS-STOR1 for surface layer storage Table-type PHOS-STOR1 for upper layer principal storage Table-type PHOS-STOR2 for upper layer transitory storage Table-type PHOS-STOR1 for lower layer storage Table-type PHOS-STOR1 for groundwater layer storage
*********************************************************** *********************
PERLND -- Section PHOS Input
438
Explanation: The exact format of each of the tables mentioned above, except for SOIL-DATA,SOIL-DATA2 and CROP-DATES, is detailed in the documentation which follows.SOIL-DATA is documented under the input for Section PEST (4.4(1).10). SOIL-DATA2and CROP-DATES are documented under the input for Section NITR (4.4(1).11). The comments given alongside the table names above indicate under whatcircumstances a table is expected. Note that if all the fields in a table havedefault values, the table can be omitted from the User's Control Input. Then, thedefaults will be adopted. However, any tables that are repeated for multiple soillayers should generally not be omitted because the "nth" occurrence of one ofthese tables refers to the corresponding "nth" layer. If a table for layer i isomitted, the next occurrence of the table (intended for layer i+1) will be appliedto layer i, and unintended results may occur.
NUPTFG and PUPTFG are the plant uptake method flag for nitrogen and phosphorus,respectively. VPUTFG and FORPFG are the phosphorus plant uptake flag and thephosphate adsorption/desorption method flag, respectively. NUPTFG is describedunder Table-type NIT-FLAGS (Sect. 4.4(1).11.1) above. The others are describedunder Table-type PHOS-FLAGS (Sect. 4.4(1).12.1) below.
PERLND -- Section PHOS Input
439
4.4(1).12.1 Table-type PHOS-FLAGS -- Flags governing simulation of phosphorus ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHOS-FLAGS <-range><-------phosflags-------> . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . END PHOS-FLAGS ******* Example ******* PHOS-FLAGS <PLS > VPUT FORP ITMX BNUM CNUM PUPT *** # - # 1 4 1 10 10 1 END PHOS-FLAGS ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<phosflags> VPUTFG 5I5 0 0 1 FORPFG 0 0 1 ITMAXP 30 1 100 BNUMP none 1 1000 CNUMP none 1 1000 PUPTFG 0 0 1----------------------------------------------------------
PERLND -- Section PHOS Input
440
Explanation If VPUTFG = 1, the first-order plant uptake parameters for phosphorus are allowedto vary throughout the year and four tables of type MON-PHOSUPT are expected inthe User's Control Input. The first appearance is for the surface layer, 2nd forupper layer, 3rd for the lower layer, and 4th for the groundwater layer. If VPUTFG= 0, the uptake rates do not vary through the year and a value for each layer isspecified in a single table (Table-type PHOS-UPTAKE).
FORPFG indicates which method is to be used to simulate adsorption and desorptionof phosphate: 0 first-order kinetics 1 single-value Freundlich method
ITMAXP is the maximum number of iterations that will be attempted in solving theFreundlich equation; applicable only if FORPFG= 1.
BNUMP is the number of time steps that will elapse between recalculation ofbiochemical reaction fluxes. For example, if BNUMP = 10 and the simulation timestep is 5 minutes, then these fluxes will be recalculated every 50 minutes. Allreactions except adsorption/desorption fall into this category. CNUMP is thecorresponding number for the chemical (adsorption/desorption) reactions.
PUPTFG indicated which method is to be used to simulate plant uptake ofphosphorus: 0 first-order kinetics 1 yield-based algorithm
PERLND -- Section PHOS Input
441
4.4(1).12.2 Table-type PHOS-AD-FLAGS -- Atmospheric deposition flags for PHOS ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHOS-AD-FLAGS <-range> <f><c> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PHOS-AD-FLAGS ******* Example ******* PHOS-AD-FLAGS <PLS > Atmospheric deposition flags *** *** PHOSPHATE ORGANIC P *** SURF UPPR SURF UPPR #*** # <F><C> <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 13 -1 END PHOS-AD-FLAGS ************************************************* ******************************* Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> PHADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------Explanation PHADFG is an array of flags indicating the source of atmospheric deposition data.Each species can be deposited into either the surface or upper soil layers. Eachspecies/layer combination has two flags. The first is for dry or total depositionflux, and the second is for wet deposition concentration. The flag valuesindicate: 0 No deposition of this type is simulated -1 Deposition of this type is input as time series PHADFX or PHADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number. (Refer to Section 4.11 for details)
PERLND -- Section PHOS Input
442
4.4(1).12.3 Table-type PHOS-FSTGEN -- Temperature correction parameters for phosphorus reactions ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHOS-FSTGEN <-range><-------------------theta------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PHOS-FSTGEN Example ******* PHOS-FSTGEN <PLS > Temperature correction parameters (theta) *** # - # THPLP THKDSP THKADP THKIMP THKMP*** 1 1.07 1.05 END PHOS-FSTGEN ****************************************************** ************************* Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<theta> THPLP 5F10.0 1.07 1.0 2.0 none Both THKDSP 1.05 1.0 2.0 none Both THKADP 1.05 1.0 2.0 none Both THKIMP 1.07 1.0 2.0 none Both THKMP 1.07 1.0 2.0 none Both-------------------------------------------------------------------------------- Explanation This table is analogous to Table-type NIT-FSTGEN, except for the first two valuesin that table. The temperature correction parameters supplied in this table (andthe reactions they affect) are:
THPLP Plant uptake (only relevant if PUPTFG=0 in Table PHOS-FLAGS) THKDSP Phosphate desorption (only relevant if FORPFG=0 in Table PHOS-FLAGS) THKADP Phosphate adsorption (only relevant if FORPFG=0 in Table PHOS-FLAGS) THKIMP Phosphate immobilization THKMP Organic P mineralization
PERLND -- Section PHOS Input
443
4.4(1).12.4 Table-type PHOS-FSTPM -- Phosphorus first-order reaction parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHOS-FSTPM <-range><---------------phos-fstpm-------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PHOS-FSTPM ******* Example ******* PHOS-FSTPM <PLS > Phosphorus first-order parameters for surface layer (/day) *** # - # KDSP KADP KIMP KMP *** 1 5 .04 END PHOS-FSTPM ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<phos-fstpm> KDSP,KADP, 4F10.0 0.0 0.0 none /day Both KIMP,KMP -------------------------------------------------------------------------------- Explanation This table is analogous to Table-type NIT-FSTPM. The reaction rate parameterssupplied in this table are:
KDSP Phosphate desorption (only used if FORPFG=0 in Table-type PHOS-FLAGS) KADP Phosphate adsorption (only used if FORPFG=0 in Table-type PHOS-FLAGS) KIMP Phosphate immobilization KMP Organic P mineralization
PERLND -- Section PHOS Input
444
4.4(1).12.5 Table-type PHOS-CMAX -- Maximum solubility of phosphate ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHOS-CMAX <-range><--cmax--> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END PHOS-CMAX ******* Example ******* PHOS-CMAX <PLS > CMAX*** # - # (ppm)*** 1 2 5.0 END PHOS-CMAX ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<cmax> CMAX F10.0 0.0 0.0 none ppm Both------------------------------------------------- ------------------------------- Explanation This table is exactly analogous to Table-type NIT-CMAX.
CMAX is the maximum solubility of phosphate in water. This table only appearsonce, and is only required if FORPFG = 1 in Table-type PHOS-FLAGS(adsorption/desorption is simulated using single-value Freundlich method).
PERLND -- Section PHOS Input
445
4.4(1).12.6 Table-type PHOS-SVALPM -- Phosphorus single value Freundlich adsorption/desorption parameters ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** PHOS-SVALPM <-range><---------svalpm-------------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PHOS-SVALPM ******* Example ******* PHOS-SVALPM <PLS > Parameters for Freundlich method (lower layer) *** # - # XFIX K1 N1 *** 1 30. 5.0 1.5 END PHOS-SVALPM ************************************************* ******************************* Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<svalpm> XFIX 3F10.0 0.0 0.0 none ppm Both K1 0.0 0.0 none Both N1 none 1.0 none Both--------------------------------------- ----------------------------------------- Explanation This table is exactly analogous to Table-type NIT-SVALPM. It is only used ifFORPFG= 1 in Table-type PHOS-FLAGS.
PERLND -- Section PHOS Input
446
4.4(1).12.7 Table-type PHOS-UPTAKE -- Phosphorus plant uptake parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHOS-UPTAKE <-range><--------------phos-uptake-------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PHOS-UPTAKE ******* Example ******* PHOS-UPTAKE <PLS > Phosphorus plant uptake parms (/day) *** # - # SKPLP UKPLP LKPLP AKPLP*** 1 .005 .03 .05 .01 END PHOS-UPTAKE ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<phos-uptake> SKPLP,UKPLP, 4F10.0 0.0 0.0 none /day Both LKPLP,AKPLP-------------------------------------------------------------------------------- Explanation This table is exactly analogous to Table-type NIT-UPTAKE.
SKPLP, UKPLP, LKPLP and AKPLP are the plant phosphorus uptake reaction rateparameters for the surface, upper, lower, and active groundwater layers,respectively. This table is required when first-order plant uptake is being used,and uptake parameters do not vary monthly (PUPTFG = 0 and VPUTFG = 0 in Table-typePHOS-FLAGS).
PERLND -- Section PHOS Input
447
4.4(1).12.8 Table-type MON-PHOSUPT -- Monthly plant uptake parameters for phosphorus, for the surface, upper, lower or groundwater layer ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** MON-PHOSUPT <-range><------------------------mon-phosupt-----------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-PHOSUPT ******* Example ******* MON-PHOSUPT <PLS > Monthly phosphorus uptake parameters for surface layer (/day)*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 2 .01 .03 .07 .07 .04 .01 END MON-PHOSUPT ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-phosupt> KPLPM(*) 12F5.0 0.0 0.0 none /day Both-------------------------------------------------------------------------------- Explanation This table is exactly analogous to Table-type MON-NITUPT.
This table is required if first-order plant uptake is being used and the plantuptake parameters vary throughout the year (PUPTFG = 0 and VPUTFG = 1 inTable-type PHOS-FLAGS). The entire table is supplied four times; first for thesurface layer, second for the upper layer, third for the lower layer, and fourthfor the active groundwater layer. If omitted, default values will be supplied.For example, if the third and fourth occurrences of the table are omitted, theparameters for the lower and groundwater layers will default to zero.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
PERLND -- Section PHOS Input
448
4.4(1).12.9 Table-type PHOS-YIELD -- Yield-based phosphorus plant uptake parameters ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** PHOS-YIELD <-range><-target-><-maxrat-> . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . END PHOS-YIELD ******* Example ******* PHOS-YIELD <PLS > PUPTGT PMXRAT *** # - # (LB/AC) *** 1 100.00 1.5 END PHOS-YIELD
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<-target-> PUPTGT F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric<-maxrat-> PMXRAT F10.0 1.0 1.0 2.0 none Both--------------------------------------------------------------------------------
Explanation This table is exactly analogous to Table-type NIT-YIELD.
PUPTGT is the total annual target for plant uptake of phosphorus for all soillayers and all crops during the calendar year.
PMXRAT is the ratio of the maximum uptake rate to the optimum (target) rate whenthe crop is making up a deficit in phosphorus uptake.
This table is required only when yield-based plant uptake is being used (i.e.,PUPTFG = 1 in Table-type PHOS-FLAGS).
PERLND -- Section PHOS Input
449
4.4(1).12.10 Table-type MON-PUPT-FR1 -- Monthly fractions for yield-based plant uptake of phosphorus ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** MON-PUPT-FR1 <-range><------------------------mon-puptfr------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-PUPT-FR1 ******* Example ******* MON-PUPT-FR1 <PLS > Monthly fractions for plant uptake target *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 .1 .2 .2 .2 .1 .2 2 .1 .1 .05 .05 .1 .1 .1 .05 .05 .1 .1 .1 END MON-PUPT-FR1 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-puptfr> PUPTFM(*) 12F5.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
Explanation
This table is exactly analogous to Table-type MON-NUPT-FR1.
These are the fractions of the total annual phosphorus plant uptake target (PUPTGTin Table-type PHOS-YIELD) applied to each month. The fractions across the yearmust sum to unity; otherwise, an error message is generated. This table is onlyrequired when yield-based plant uptake of phosphorus is being used (PUPTFG = 1 inTable-type PHOS-FLAGS).
PERLND -- Section PHOS Input
450
4.4(1).12.11 Table-type MON-PUPT-FR2 -- Monthly fractions for yield-based plant uptake of phosphorus from a soil layer ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** MON-PUPT-FR2 <-range><------------------------mon-layfr-------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-PUPT-FR2 ******* Example ******* MON-PUPT-FR2 <PLS > Monthly fractions for plant uptake target from surface *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 2 .15 .15 .15 .1 .1 .1 .1 .1 .15 .12 .12 .1 END MON-PUPT-FR2 ************************************************* ******************************* Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-layfr> SPUPTM(*),UPUPTM(*) 12F5.0 0.0 0.0 1.0 none Both LPUPTM(*),APUPTM(*)--------------------------------------------------------------------------------
Explanation
This table is exactly analogous to Table-type MON-NUPT-FR2. Refer to that tablefor details.
PERLND -- Section PHOS Input
451
4.4(1).12.12 Table-type PHOS-STOR1 -- Initial phosphorus storage in the surface, upper, lower or groundwater layer ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** PHOS-STOR1 <-range><--------------phos-stor1--------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PHOS-STOR1 ******* Example ******* PHOS-STOR1 <PLS >Initial phosphorus in upper layer (lb/ac) *** # - # ORGP P4AD P4SU PLTP *** 1 3 50. 2000. 200. END PHOS-STOR1 ************************************************* ******************************* Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<phos-stor1> ORGP,P4AD, 4F10.0 0.0 0.0 none lb/ac Engl P4SU,PLTP 0.0 0.0 none kg/ha Metric-------------------------------------------------------------------------------- Explanation This table is analogous to Table-type NIT-STOR1. It specifies the initial storageof P in one of the four major soil layers. The values in the table are:
ORGP Organic phosphorus P4AD Adsorbed phosphate P4SU Solution phosphate PLTP Phosphorus stored in plants
PERLND -- Section PHOS Input
452
4.4(1).12.13 Table-type PHOS-STOR2 -- Initial storage of phosphate in upper layer transitory (interflow) storage ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** PHOS-STOR2 <-range><--phos--> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END PHOS-STOR2 ******* Example ******* PHOS-STOR2 <PLS >Phosphate in interflow (kg/ha) *** # - # IP4SU *** 1 6 100. END PHOS-STOR2 ************************************************* ******************************* Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<phos> IP4SU F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric--------------------------------------------------------------------------------
Explanation
This table is analogous to Table-type NIT-STOR2. It specifies the initial storageof solution phosphate in the upper layer transitory (interflow) storage.
PERLND -- Section TRACER Input
453
4.4(1).13 PERLND BLOCK -- Section TRACER input ******************************************************************************** 1 2 3 4 5 6 7 81234567890123456789012345678901234567890123456789 0123456789012345678901234567890********************************************************************************Layout****** Table-type TRAC-ID [Table-type TRAC-AD-FLAGS] [Table-type TRAC-TOPSTOR] [Table-type TRAC-SUBSTOR]
******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Note: if all the fields in a table have default values, the table can be omittedfrom the User's Control Input. Then, the defaults will be adopted.
PERLND -- Section TRACER Input
454
4.4(1).13.1 Table-type TRAC-ID -- Name of conservative substance (tracer) ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** TRAC-ID <-range><-----trac-id------> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END TRAC-ID ******* Example ******* TRAC-ID <PLS >Name of tracer *** # - # *** 1 10 Chloride END TRAC-ID ******************************************************************************** Details ----------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------- <trac-id> TRACID(*) 5A4 none none none----------------------------------------------------------- Explanation Any 20 character string can be supplied as the name of the tracer substance.
PERLND -- Section TRACER Input
455
4.4(1).13.2 Table-type TRAC-AD-FLAGS -- Atmospheric deposition flags for TRACER ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** TRAC-AD-FLAGS <-range> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END TRAC-AD-FLAGS ******* Example ******* TRAC-AD-FLAGS <PLS > Atmospheric deposition flags *** *** SURF UPPR #*** # <F><C> <F><C> 1 7 -1 10 -1 -1 END TRAC-AD-FLAGS ******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> TRADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------Explanation TRADFG is an array of flags indicating the source of atmospheric deposition data.The tracer substance can be deposited into either the surface or upper soillayers. Each layer has two flags. The first is for dry or total deposition flux,and the second is for wet deposition concentration. The flag values indicate:
0 No deposition of this type is simulated -1 Deposition of this type is input as time series TRADFX or TRADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number. (Refer to Section 4.11 for details)
PERLND -- Section TRACER Input
456
4.4(1).13.3 Table-type TRAC-TOPSTOR -- Initial quantity of tracer in topsoil storages ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** TRAC-TOPSTOR <-range><---------trac-topstor-------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END TRAC-TOPSTOR ******* Example ******* TRAC-TOPSTOR <PLS >Initial storage of chloride in topsoil (kg/ha) *** # - # STRSU UTRSU ITRSU *** 1 200. END TRAC-TOPSTOR ************************************************* ******************************* Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<trac-topstor> STRSU,UTRSU, 3F10.0 0.0 0.0 none lb/ac Engl ITRSU 0.0 0.0 none kg/ha Metric-------------------------------------------------------------------------------- Explanation This table specifies the initial storage of tracer (conservative) in the surface,upper principal, and upper transitory (interflow) storages.
PERLND -- Section TRACER Input
457
4.4(1).13.4 Table-type TRAC-SUBSTOR -- Initial quantity of tracer in lower and active groundwater storages ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** TRAC-SUBSTOR <-range><---trac-substor---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END TRAC-SUBSTOR ******* Example ******* TRAC-SUBSTOR <PLS >Initial storage of chloride in subsoil layers (lb/ac) *** # - # LTRSU ATRSU *** 1 300. 500. END TRAC-SUBSTOR ************************************************* ******************************* Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<trac-substor> LTRSU,ATRSU 2F10.0 0.0 0.0 none lb/ac Engl 0.0 0.0 none kg/ha Metric-------------------------------------------------------------------------------- Explanation This table specifies the initial storage of conservative (tracer) material in thelower and active groundwater layers.
IMPLND Block
458
4.4(2) IMPLND Block ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IMPLND General input [section ATEMP input] [section SNOW input] [section IWATER input] [section SOLIDS input] [section IWTGAS input] [section IQUAL input]END IMPLND ************************************************* ******************************* Explanation This block contains the data which are domestic to all the Impervious Land-segments in the RUN. The General input is always relevant: other input is onlyrequired if the module section concerned is active. 4.4(2).1 IMPLND BLOCK -- General input
*************************************** ***************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789 012345678901234567890********************************************************************************Layout****** Table-type ACTIVITY [Table-type PRINT-INFO] Table-type GEN-INFO ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
IMPLND -- General Input
459
4.4(2).1.1 Table-type ACTIVITY -- Active Sections Vector ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** ACTIVITY <-range><---------a-s-vector---------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END ACTIVITY ******* Example ******* ACTIVITY <ILS > Active Sections *** # - # ATMP SNOW IWAT SLD IWG IQAL *** 1 7 1 1 1 9 0 0 0 1 END ACTIVITY ************************************************* ******************************* Details ----------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------------------<a-s-vector> AIRTFG,SNOWFG,IWATFG, 6I5 0 0 1 SLDFG,IWGFG,IQALFG---------------------------------------------------------------------- Explanation The IMPLND module is divided into 6 sections. The values supplied in this tablespecify which sections are active and which are not, for each operation involvingthe IMPLND module. A value of 0 means inactive and 1 means active. Any meaningfulsubset of sections may be active.
IMPLND -- General Input
460
4.4(2).1.2 Table-type PRINT-INFO -- Printout information for IMPLND ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PRINT-INFO <-range><--------print-flags---------><piv><pyr> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PRINT-INFO ******* Example ******* PRINT-INFO <ILS > ******** Print-flags ******** PIVL PYR # - # ATMP SNOW IWAT SLD IWG IQAL ********* 1 7 2 4 6 10 12 END PRINT-INFO ******************************************************************************** Details ------------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------- <print-flags> PFLAG(6) 6I5 4 2 6 <piv> PIVL I5 1 1 1440 <pyr> PYREND I5 9 1 12-------------------------------------------------------------
IMPLND -- General Input
461
Explanation HSPF permits the user to vary the printout level (maximum frequency) for thevarious active sections of an operation. The meaning of each permissible valuefor PFLAG() is: 2 means every PIVL intervals 3 means every day 4 means every month 5 means every year 6 means never In the example above, output from Impervious Land-segments 1 thru 7 will occur asfollows: Section Maximum frequency ATEMP 10 intervalsSNOW month IWATER never SOLIDS -- thru | month (defaulted)IQUAL -- A value need only be supplied for PIVL if one or more sections have a printoutlevel of 2. For those sections, printout will occur every PIVL intervals (thatis, every PDELT=PIVL*DELT minutes). PIVL must be chosen such that there are aninteger number of PDELT periods in a day. HSPF will automatically provide printed output at all standard intervals greaterthan the specified minimum interval. In the above example, output for sectionATEMP will be printed at the end of each 10 intervals, day, month and year. PYREND is the calendar month which will terminate the year for printout purposes.Thus, the annual summary can reflect the situation over the past water year or thepast calendar year, etc.
IMPLND -- General Input
462
4.4(2).1.3 Table-type GEN-INFO -- Other general information for IMPLND ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GEN-INFO <-range><---ILS-id---------> <unitsyst><-printu-> . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . END GEN-INFO ******* Example ******* GEN-INFO <ILS > Name UnitSysts Printout *** # - # t-series Engl Metr *** in out *** 1 Chicago loop 2 Astrodome 1 23 END GEN-INFO ******************************************************************************** Details ------------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------- <ILS-id> LSID(5) 5A4 none none none <unit-syst> IUNITS,OUNITS 2I5 1 1 2 <printu> PUNIT(2) 2I5 0 0 99------------------------------------------------------------- Explanation Any string of up to 20 characters may be supplied as the identifier for an IMPLND.
The values supplied for IUNITS and OUNITS indicate the system of units for inputand output time series, respectively. 1 means English units, 2 means Metric units.
IMPLND -- General Input
463
Note: All operations in the run must use the same units system for data in the UCIfile; therefore, this system of units is specified by EMFG in the GLOBAL block.
The values supplied for PUNIT(*) indicate the destinations (files) of printout inEnglish and metric units, respectively. A value of 0 means no printout isrequired in that unit system. A non-zero value means printout is required in thatsystem and is the unit number of the file to which printout is to be written. Theunit number is associated with a filename in the FILES BLOCK.
Note that printout for each Impervious Land Segment can be obtained in either theEnglish or Metric systems, or both (irrespective of the system used to supply theinputs).
IMPLND -- Section IWATER Input
464
4.4(2).2 IMPLND BLOCK -- SECTION ATEMP INPUT Section ATEMP is common to the PERLND and IMPLND modules. See Section 4.4(1).2for documentation.
4.4(2).3 IMPLND BLOCK -- SECTION SNOW INPUT Section SNOW is common to the PERLND and IMPLND modules. See Section 4.4(1).3 fordocumentation.
4.4(2).4 IMPLND BLOCK -- Section IWATER input ******************************************************************************** 1 2 3 4 5 6 7 81234567890123456789012345678901234567890123456789 0123456789012345678901234567890********************************************************************************Layout****** [Table-type IWAT-PARM1 ] Table-type IWAT-PARM2 [Table-type IWAT-PARM3 ] --- [Table-type MON-RETN ] | only required if the relevant quantity [Table-type MON-MANNING] | varies through the year --- [Table-type IWAT-STATE1] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
IMPLND -- Section IWATER Input
465
4.4(2).4.1 Table-type IWAT-PARM1 -- First group of IWATER parameters (flags) ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IWAT-PARM1 <-range><-------iwatparm1-------> . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . END IWAT-PARM1 ******* Example ******* IWAT-PARM1 <ILS > Flags *** # - # CSNO RTOP VRS VNN RTLI *** 1 7 1 1 END IWAT-PARM1 ******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<iwatparm1> CSNOFG,RTOPFG, 5I5 0 0 1 VRSFG,VNNFG, RTLIFG ------------------------------------------------------------
IMPLND -- Section IWATER Input
466
Explanation If CSNOFG is 1, section IWATER assumes that snow accumulation and melt is beingconsidered. It will, therefore, expect that the time series produced by sectionSNOW are available, either internally (produced in this RUN) or from externalsources (produced in a previous RUN). If CSNOFG is 0, no such time series areexpected. See the functional description for further information.
RTOPFG is a flag that selects the algorithm for computing overland flow. Twooptional methods are provided. If RTOPFG is 1, routing of overland flow is donein the same way as in the NPS Model. A value of 0 results in a differentalgorithm. (See the functional description for details).
The flags beginning with "V" indicate whether or not certain parameters will beassumed to vary through the year: 1 means they do vary, 0 means they do not. Thequantities concerned are: VRSFG retention storage capacity VNNFG Manning's n for the overland flow plane If either of these flags are on, monthly values for the parameter concerned mustbe supplied (see Table-types MON-RETN and MON-MANNING). If RTLIFG is 1, any lateral surface inflow to the ILS will be subject to retentionstorage; if it is 0, lateral inflow is not subject to retention storage.
IMPLND -- Section IWATER Input
467
4.4(2).4.2 Table-type IWAT-PARM2 -- Second group of IWATER parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IWAT-PARM2 <-range><-------------iwatparm2-----------------> . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . END IWAT-PARM2 ******* Example ******* IWAT-PARM2 <ILS > *** # - # LSUR SLSUR NSUR RETSC *** 1 7 400. .001 END IWAT-PARM2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<iwatparm2> LSUR F10.0 none 1.0 none ft Engl none 0.3 none m Metric SLSUR F10.0 none .000001 10. none Both NSUR F10.0 0.1 0.001 1.0 Both RETSC F10.0 0.0 0.0 10.0 in Engl 0.0 0.0 250. mm Metric-------------------------------------------------------------------------------- Explanation LSUR is the length of the assumed overland flow plane, and SLSUR is the slope. NSUR is Manning's n for the overland flow plane. RETSC is the retention (interception) storage capacity of the surface.
IMPLND -- Section IWATER Input
468
4.4(2).4.3 Table-type IWAT-PARM3 -- Third group of IWATER parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IWAT-PARM3 <-range><----iwatparm3-----> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END IWAT-PARM3 ******* Example ******* IWAT-PARM3 <ILS >*** # - #*** PETMAX PETMIN 1 7 9 39 33 END IWAT-PARM3 ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<iwatparm3> PETMAX F10.0 40. none none degF Engl 4.4 none none degC Metric PETMIN F10.0 35. none none degF Engl 1.7 none none degC Metric--------------------------------------- ----------------------------------------- Explanation PETMAX is the air temperature below which E-T will arbitrarily be reduced belowthe value obtained from the input time series.
PETMIN is the temperature below which E-T will be zero regardless of the value inthe input time series. These values are only used if snow is being considered(i.e., CSNOFG= 1 in Table-type IWAT-PARM1).
IMPLND -- Section IWATER Input
469
4.4(2).4.4 Table-type MON-RETN -- Monthly retention storage capacity ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-RETN <-range><-----------------mon-retn---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-RETN ******* Example ******* MON-RETN <ILS > Retention storage capacity at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .02 .03 .03 .04 .05 .08 .12 .15 .12 .05 .03 .01 END MON-RETN ************************************************* ******************************* Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-retn> RETSCM(12) 12F5.0 0.0 0.0 10. in Engl 0.0 0.0 250. mm Metric-------------------------------------------------------------------------------- Explanation This table is only required if VRSFG = 1 in Table-type IWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
IMPLND -- Section IWATER Input
470
4.4(2).4.5 Table-type MON-MANNING -- Monthly Manning's n values ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-MANNING <-range><----------------mon-Manning-------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-MANNING ******* Example ******* MON-MANNING <ILS > Manning's n at start of each month *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC *** 1 7 .23 .34 .34 .35 .28 .35 .37 .35 .28 .29 .30 .30 END MON-MANNING ******************************************************************************** Details --------------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units Unit name(s) system --------------------------------------------------------------------------- <mon-Manning> NSURM(12) 12F5.0 0.1 .001 1.0 complex Both --------------------------------------------------------------------------- Explanation This table is only required if VNNFG = 1 in Table-type IWAT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
IMPLND -- Section IWATER Input
471
4.4(2).4.6 Table-type IWAT-STATE1 -- IWATER initial state variables
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
IWAT-STATE1 <-range><----iwat-state1---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END IWAT-STATE1 ******* Example ******* IWAT-STATE1 <ILS > IWATER state variables*** # - #*** RETS SURS 1 7 0.05 0.10 END IWAT-STATE1 ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<iwat-state1> RETS 2F10.0 .001 .001 100 inches Engl .025 .025 2500 mm Metric SURS .001 .001 100 inches Engl .025 .025 2500 mm Metric--------------------------------------- ----------------------------------------- Explanation This table is used to specify the initial water storages.
RETS is the initial retention storage.
SURS is the initial surface (overland flow) storage.
IMPLND -- Section SOLIDS Input
472
4.4(2).5 IMPLND BLOCK -- Section SOLIDS input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type SLD-PARM1] Table-type SLD-PARM2 [Table-type MON-SACCUM] [Table-type MON-REMOV] [Table-type SLD-STOR ] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
Tables enclosed in brackets [] above are not always required; for example, becauseall the values can be defaulted.
IMPLND -- Section SOLIDS Input
473
4.4(2).5.1 Table-type SLD-PARM1 -- First group of SOLIDS parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SLD-PARM1 <-range><--sld-parm1--> . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . END SLD-PARM1 ******* Example ******* SLD-PARM1 <ILS > *** # - # VASD VRSD SDOP*** 1 7 0 1 0 END SLD-PARM1 ******************************************************************************** Details ----------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------- <sld-parm1> VASDFG 3I5 0 0 1 VRSDFG 0 0 1 SDOPFG 0 0 1 ----------------------------------------------------------- Explanation If VASDFG is 1, the accumulation rate of solids is allowed to vary throughout theyear and Table-type MON-SACCUM is expected. If the flag is zero, the accumulationrate is constant, and is specified in Table-type SLD-PARM2. The correspondingflag for the unit removal rate is VRSDFG.
SDOPFG is a flag that determines the algorithm used to simulate removal ofsediment from the land surface. If SDOPFG is 1, sediment removal will besimulated with the algorithm used in the NPS model. If it is 0, a differentalgorithm will be used. (See the functional description for details).
IMPLND -- Section SOLIDS Input
474
4.4(2).5.2 Table-type SLD-PARM2 -- Second group of SOLIDS parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SLD-PARM2 <-range><-------------sld-parm2----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END SLD-PARM2 Example ******* SLD-PARM2 <ILS >*** # - # KEIM JEIM ACCSDP REMSDP*** 1 7 0.08 1.90 0.01 0.5 END SLD-PARM2 *************************************** ***************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sld-parm2> KEIM 4F10.0 0.0 0.0 none complex Both JEIM none none none complex Both ACCSDP 0.0 0.0 none tons/ac/day Engl 0.0 0.0 none tonnes/ha/day Metric REMSDP 0.0 0.0 1.0 /day Both-------------------------------------------------------------------------------- Explanation KEIM is the coefficient in the solids washoff equation.
JEIM is the exponent in the solids washoff equation.
ACCSDP is the rate at which solids accumulate on the land surface.
REMSDP is the fraction of solids storage which is removed each day when there isno runoff, for example, because of street sweeping.
If monthly values for the accumulation and unit removal rates are being supplied,values supplied for these variables in this table are not used by the program.
IMPLND -- Section SOLIDS Input
475
4.4(2).5.3 Table-type MON-SACCUM -- Monthly solids accumulation rates ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-SACCUM <-range><---------------mon-accum----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-SACCUM ******* Example ******* MON-SACCUM <ILS > Monthly values for solids accumulation (tonnes/ha.day) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 0.0 .12 .12 .24 .24 .56 .67 .56 .34 .34 .23 .12 END MON-SACCUM ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-accum> ACCSDM(12) 12F5.0 0.0 0.0 none tons/ac/day Engl 0.0 0.0 none tonnes/ha/day Metr------------------------------------------------- ------------------------------- Explanation This table is only required if VASDFG = 1 in Table-type SLD-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
IMPLND -- Section SOLIDS Input
476
4.4(2).5.4 Table-type MON-REMOV -- Monthly solids unit removal rates ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-REMOV <-range><---------------mon-remov----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-REMOV ******* Example ******* MON-REMOV <ILS > Monthly solids unit removal rate *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .05 .05 .07 .15 .15 .20 .20 .20 .20 .10 .05 .05 END MON-REMOV ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------- ---------------------<mon-remov> REMSDM(12) 12F5.0 0.0 0.0 1.0 /day Both -------------------------------------------------------------------------------- Explanation This table is only required if VRSDFG = 1 in Table-type SLD-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
IMPLND -- Section SOLIDS Input
477
4.4(2).5.5 Table-type SLD-STOR -- Initial solids storage ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SLD-STOR <-range><sld-stor> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END SLD-STOR ******* Example ******* SLD-STOR <ILS > Solids storage (tons/acre) *** # - # *** 1 7 0.2 END SLD-STOR ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sld-stor> SLDS F10.0 0.0 0.0 none tons/ac Engl 0.0 0.0 none tonnes/ha Metric-------------------------------------------------------------------------------- Explanation SLDS is the initial storage of solids on the impervious surface.
IMPLND -- Section IWTGAS Input
478
4.4(2).6 IMPLND BLOCK -- Section IWTGAS input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type IWT-PARM1] [Table-type IWT-PARM2] Tables in brackets [] are not [Table-type MON-AWTF] always required [Table-type MON-BWTF] [Table-type IWT-INIT] ******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
IMPLND -- Section IWTGAS Input
479
4.4(2).6.1 Table-type IWT-PARM1 -- Flags for section IWTGAS ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IWT-PARM1 <-range><iwtparm1> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END IWT-PARM1 ******* Example ******* IWT-PARM1 <ILS > Flags for section IWTGAS*** # - # WTFV CSNO *** 1 7 0 0 END IWT-PARM1 ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------- <iwtparm1> WTFVFG 2I5 0 0 1 CSNOFG 0 0 1 ----------------------------------------------------------- Explanation WTFVFG indicates whether or not the water temperature regression parameters (AWTFand BWTF) are allowed to vary throughout the year, and thus, whether or notTable-types MON-AWTF and MON-BWTF are expected. If CSNOFG=1, the effects of snow accumulation and melt are being considered; ifit is zero, they are not. If section IWATER is active, the value of CSNOFGsupplied here is ignored because it was first supplied in the input for thatsection (Table-type IWAT-PARM1).
IMPLND -- Section IWTGAS Input
480
4.4(2).6.2 Table-type IWT-PARM2 -- Second group of IWTGAS parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IWT-PARM2 <-range><---------iwt-parm2----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END IWT-PARM2 ******* Example ******* IWT-PARM2 <ILS > Second group of IWTGAS parms*** # - # ELEV AWTF BWTF*** 1 7 1281. 40.0 0.8 END IWT-PARM2 ******************************************************************************** Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<iwt-parm2> ELEV 3F10.0 0.0 -1000. 30000. ft Engl 0.0 -300. 9100. m Metric AWTF 32. 0.0 100. DegF Engl 0.0 -18. 38. DegC Metr BWTF 1.0 0.001 2.0 DegF/F Engl 1.0 0.001 2.0 DegC/C Metr--------------------------------------- ----------------------------------------- Explanation ELEV is the elevation of the ILS above sea level; it is used to adjust saturationconcentrations of dissolved gases in surface outflow. AWTF is the surface water temperature when the air temperature is 32 deg F (0 degC). It is the intercept of the surface water temperature regression equation.
BWTF is the slope of the surface water temperature regression equation.
IMPLND -- Section IWTGAS Input
481
4.4(2).6.3 Table-type MON-AWTF -- Monthly values for AWTF ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-AWTF <-range><-----------------mon-awtf---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-AWTF ******* Example ******* MON-AWTF <ILS > Value of AWTF at start of each month (deg F) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 37. 38. 39. 40. 41. 42. 43. 44. 45. 44. 41. 40. END MON-AWTF ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-awtf> AWTFM(12) 12F5.0 32. 0. 100. deg F Engl 0. -18. 38. deg C Metric-------------------------------------------------------------------------------- Explanation This table is only required if WTFVFG = 1 in Table-type IWT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
IMPLND -- Section IWTGAS Input
482
4.4(2).6.4 Table-type MON-BWTF -- Monthly values for BWTF ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MON-BWTF <-range><-----------------mon-bwtf---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-BWTF ******* Example ******* MON-BWTF <ILS > Value of BWTF at start of each month (deg F/F) *** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .3 .3 .3 .4 .4 .5 .5 .5 .4 .4 .4 .3 END MON-BWTF ************************************************* ******************************* Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<mon-bwtf> BWTFM(12) 12F5.0 1.0 0.001 2.0 deg F/F Engl 1.0 0.001 2.0 deg C/C Metric-------------------------------------------------------------------------------- Explanation This table is only required if WTFVFG = 1 in Table-type IWT-PARM1.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
IMPLND -- Section IWTGAS Input
483
4.4(2).6.5 Table-type IWT-INIT -- Initial conditions for section IWTGAS ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IWT-INIT <-range><---------iwt-init-----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END IWT-INIT ******* Example ******* IWT-INIT <ILS > SOTMP SODOX SOCO2*** # - # DegC mg/l mg C/l*** 1 7 16. END IWT-INIT ******************************************************************************** Details --------------------------------------- -----------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<iwt-init> SOTMP 3F10.0 60.0 32. 100. Deg F Engl 16.0 .01 38.0 Deg C Metric SODOX 0.0 0.0 20.0 mg/l Both SOCO2 0.0 0.0 1.0 mg C/l Both--------------------------------------- ----------------------------------------- Explanation These are the initial values for the temperature, dissolved oxygen concentration,and CO2 concentration of the surface runoff. The values given in this table donot affect anything in the simulation beyond the start of the first interval ofthe run. Therefore, in most simulations, this table can be omitted.
IMPLND -- Section IQUAL Input
484
4.4(2).7 IMPLND BLOCK -- Section IQUAL input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type NQUALS] [Table-type IQL-AD-FLAGS] --- Table-type QUAL-PROPS | [Table-type QUAL-INPUT] | [Table-type MON-POTFW] | repeat this group of tables for each [Table-type MON-ACCUM] | quality constituent [Table-type MON-SQOLIM] | ---******************************************************************************** Explanation The exact format of each of the tables mentioned above is detailed in thedocumentation which follows or in the documentation for the PERLND module. Tables enclosed in brackets [] are not always required;for example, because allthe values can be defaulted. 4.4(2).7.1 Table-type NQUALS -- Total number of quality constituents simulated This table is identical to the corresponding table for the PERLND module. SeeSection 4.4(1).8.1 (Module Section PQUAL) for documentation.
IMPLND -- Section IQUAL Input
485
4.4(2).7.2 Table-type IQL-AD-FLAGS -- Atmospheric deposition flags for IQUAL
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** IQL-AD-FLAGS <-range> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END IQL-AD-FLAGS ******* Example ******* IQL-AD-FLAGS <ILS > Atmospheric deposition flags *** *** QUAL1 QUAL2 QUAL3 QUAL4 QUAL5 QUAL6 QUAL7 QUAL8 QUAL9 QAL10 #*** # <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 13 -1 0 0 0 11 0 -1 0 0 -1 0 END IQL-AD-FLAGS ******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> IQADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation IQADFG is an array of flags indicating the source of atmospheric deposition datafor QUALs. Each QUAL has two flags. The first is for dry or total depositionflux, and the second is for wet deposition concentration. The flag valuesindicate: 0 No deposition of this type is simulated -1 Deposition of this type is input as time series IQADFX or IQADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number. (Refer to Section 4.11 for details)
Note: atmospheric deposition can only be specified for QUALOF's; it is an errorto specify a non-zero flag value for a non-QUALOF.
IMPLND -- Section IQUAL Input
486
4.4(2).7.3 Table-type QUAL-PROPS -- Identifiers and flags for a quality constituent ************************************************* ******************************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890*************************************** *****************************************Layout****** QUAL-PROPS <-range><-qualid---> <qt><------flags-------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END QUAL-PROPS ******* Example ******* QUAL-PROPS <ILS > Identifiers and Flags *** # - # QUALID QTID QSD VPFW QSO VQO*** 1 7 B0D kg 0 0 1 1 END QUAL-PROPS ************************************************* ******************************* Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<qualid> QUALID 3A4 none none none<qt> QTYID A4 none none none<flags> QSDFG 4I5 0 0 1 VPFWFG 0 0 1 QSOFG 0 0 1 VQOFG 0 0 1 ----------------------------------------------------------
IMPLND -- Section IQUAL Input
487
Explanation QUALID is a string of up to 10 characters which identifies the qualityconstituent.
QTYID is a string of up to 4 characters which identifies the units associated withthis constituent (e.g., kg, # (for coliforms)). These are the units referred toas "qty" in subsequent tables (e.g., Table-type QUAL-INPUT). If QSDFG is 1 then:
1. This constituent is a QUALSD (sediment associated). 2. If VPFWFG is 1, the washoff potency factor may vary throughout the year.
Table-type MON-POTFW is expected. If QSOFG is 1 then:
1. This constituent is a QUALOF (directly associated with overland flow).2. If VQOFG is 1, the rate of accumulation and the limiting storage of QUALOF
may vary throughout the year. Table-types MON-ACCUM and MON-SQOLIM areexpected.
4.4(2).7.4 Table-type QUAL-INPUT -- Surface storage of qual and nonseasonal parameters *********************************************************** ********************* 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890************************************************* *******************************Layout****** QUAL-INPUT <-range><------------qual-input----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END QUAL-INPUT ******* Example ******* QUAL-INPUT <ILS > Storage on surface and nonseasonal parameters*** # - # SQO POTFW ACQOP SQOLIM WSQOP *** 1 7 1.21 .172 0.02 2.0 1.70 END QUAL-INPUT *********************************************************** *********************
IMPLND -- Section IQUAL Input
488
Details
--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<qual-input> SQO 5F8.0 0.0 0.0 none qty/ac Engl 0.0 0.0 none qty/ha Metric POTFW 0.0 0.0 none qty/ton Engl 0.0 0.0 none qty/tonne Metric ACQOP 0.0 0.0 none qty/ac/day Engl 0.0 0.0 none qty/ha/day Metric SQOLIM 0.000001 0.000001 none qty/ac Engl 0.000002 0.000002 none qty/ha Metric WSQOP 1.64 0.01 none in/hr Engl 41.7 0.25 none mm/hr Metric-------------------------------------------------------------------------------- Explanation The following variable is relevant only if the constituent is a QUALSD:
1. POTFW, the washoff potency factor. POTFW (washoff potency factor) is the ratio of constituent yield to sedimentoutflow. The following variables are applicable only if the constituent is a QUALOF:
1. SQO, the initial storage of QUALOF on the surface of the ILS. 2. ACQOP, the rate of accumulation of QUALOF on the surface.3. SQOLIM, the maximum storage of QUALOF on the surface.4. WSQOP, the rate of surface runoff that will remove 90 percent of stored
QUALOF per hour. If monthly values are being supplied for any of these quantities, the value inthis table is not relevant; instead,the system expects and uses values suppliedin the corresponding monthly table (Table-types MON-POTFW, MON-ACCUM, MON-SQOLIM).
IMPLND -- Section IQUAL Input
489
4.4(2).7.5 Table-type MON-POTFW -- Monthly washoff potency factor This table is identical to the corresponding table in the PERLND module. SeeSection 4.4(1).8 for documentation.
4.4(2).7.6 Table-type MON-ACCUM -- Monthly accumulation rates of QUALOF
This table is identical to the corresponding table in the PERLND module. SeeSection 4.4(1).8 for documentation.
4.4(2).7.7 Table-type MON-SQOLIM -- Monthly limiting storage of QUALOF This table is identical to the corresponding table in the PERLND module. SeeSection 4.4(1).8 for documentation.
RCHRES Block
489
4.4(3) RCHRES Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
RCHRES General input [section HYDR input] [section ADCALC input] [section CONS input] [section HTRCH input] [section SEDTRN input] [section GQUAL input] [input for RQUAL sections] [section OXRX input] [section NUTRX input] [section PLANK input] [section PHCARB input] END RCHRES********************************************************************************
Explanation
This block contains the data that are domestic to all RCHRES processing units inthe RUN. The general input is always relevant; other input is only required if themodule section concerned is active.
4.4(3).1 RCHRES BLOCK -- General input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
Table-type ACTIVITY [Table-type PRINT-INFO] Table-type GEN-INFO ********************************************************************************
Explanation
The exact format of each of the tables mentioned above is detailed in thedocumentation which follows. Tables enclosed in brackets [], above, are not alwaysrequired; for example, because all values can be defaulted.
RCHRES -- General Input
490
4.4(3).1.1 Table-type ACTIVITY -- Active Sections Vector
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout:
ACTIVITY <-range><-----------------a-s-vector--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END ACTIVITY
Example:
ACTIVITY RCHRES Active sections*** # - # HYFG ADFG CNFG HTFG SDFG GQFG OXFG NUFG PKFG PHFG *** 1 7 1 1 1 1 1 1 1 0 0 0 END ACTIVITY
********************************************************************************
Details -------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)-------------------------------------------------------------------------<a-s-vector> HYDRFG,ADFG,CONSFG,HTFG,SEDFG 10I5 0 0 1 GQALFG,OXFG,NUTFG,PLKFG,PHFG-------------------------------------------------------------------------
Explanation
The RCHRES module is divided into eleven sections. The values supplied in thistable specify which sections are active and which are not, for each operationinvolving the RCHRES module. A value of 0 means inactive and 1 means active (seebelow). Any meaningful subset of sections may be active, with the followingprovisos: 1) Section ADCALC must be active if any "quality" sections (CONS throughPHCARB) are active. 2) If any section in the RQUAL group (Section OXRX throughPHCARB) is active, all preceding RQUAL sections must also be active.
RCHRES -- General Input
491
4.4(3).1.2 Table-type PRINT-INFO -- Printout information for RCHRES
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PRINT-INFO <-range><---------------print-flags----------------------><piv><pyr> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PRINT-INFO
******* Example *******
PRINT-INFO RCHRES Printout level flags*** # - # HYDR ADCA CONS HEAT SED GQL OXRX NUTR PLNK PHCB PIVL PYR*** 1 7 2 2 2 5 5 2 3 3 10 12 END PRINT-INFO
********************************************************************************
Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<print-flags> PFLAG(10) 10I5 4 2 6 <pivl> PIVL I5 1 1 1440<pyr> PYREND I5 9 1 12----------------------------------------------------------
RCHRES -- General Input
492
Explanation
HSPF permits the user to vary the printout level (maximum frequency) for thevarious active sections of an operation. The meaning of each permissible value forPFLAG(*) is:
2 means every PIVL intervals 3 means every day 4 means every month 5 means every year 6 means never
In the example above, output from RCHRESs 1 through 7 will occur as follows:
Section Maximum frequency
HYDR 10 intervals ADCALC 10 intervals CONS 10 intervals HTRCH year SEDTRN year GQUAL 10 intervals OXRX dayNUTRX dayPLANK month (defaulted)PHCARB month (defaulted)
A value need only be supplied for PIVL if one or more sections have a printoutlevel of 2. For those sections, printout will occur every PIVL intervals (that is,every PDELT=PIVL*DELT minutes. PIVL must be chosen such that there are an integernumber of PDELT periods in a day.
HSPF will automatically provide printed output at all standard intervals greaterthan the specified minimum interval. In the above example, output for sectionNUTRX will be printed at the end of each day, month, and year.
PYREND is the calendar month which will terminate the year for printout purposes.Thus, the annual summary can reflect the situation over the past water year or thepast calendar year, etc.
RCHRES -- General Input
493
4.4(3).1.3 Table-type GEN-INFO -- Other general information for RCHRES
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GEN-INFO <-range><-----rchid--------><nex> <unitsyst><-printu-><lak> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GEN-INFO
******* Example *******
GEN-INFO RCHRES Name Nexits UnitSysts Printout *** # - # t-series Engl Metr LKFG *** in out *** 4 East River-mile 4 2 1 1 23 0 END GEN-INFO
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<rchid> RCHID(5) 5A4 none none none<nex> NEXITS I5 1 1 5 <unit-syst> IUNITS 5X,I5 1 1 2 OUNITS I5 1 1 2 <printu> PUNIT(2) 2I5 0 0 99<lak> LKFG I5 0 0 1 ----------------------------------------------------------
RCHRES -- General Input
494
Explanation
Any string of up to 20 characters may be supplied as the identifier for a RCHRES.
NEXITS is the number of exits from the RCHRES. A maximum of 5 exits can be handled.
The values supplied for IUNITS and OUNITS indicate the system of units for inputand output time series, respectively. 1 means English units, 2 means Metric units.
Note: All operations in the run must use the same units system for data in the UCIfile; therefore, this system of units is specified by EMFG in the GLOBAL block.
The values supplied for PUNIT(*) indicate the destinations (files) of printout inEnglish and metric units, respectively. A value of 0 means no printout is requiredin that unit system. A non-zero value means printout is required in that systemand is the unit number of the file to which printout is to be written. The unitnumber is associated with a filename in the FILES BLOCK.
LKFG indicates whether the RCHRES is a lake (1) or a stream/river (0). This flagaffects the method of calculating bed shear stress (in Section HYDR) and thereaeration coefficient (Section OXRX).
RCHRES -- Section HYDR Input
495
4.4(3).2 RCHRES BLOCK -- Section HYDR input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
[Table-type HYDR-PARM1] Table-type HYDR-PARM2 [Table-type MON-CONVF] --- if VCONFG = 1 (in Table-type HYDR-PARM1) [Table-type HYDR-INIT] --- [Table-type HYDR-CATEGORY] | [Table-type HYDR-CINIT] | [Table-type HYDR-CPREC] | if "categories" are being simulated [Table-type HYDR-CEVAP] | [Table-type HYDR-CFVOL] | [Table-type HYDR-CDEMAND] | ---********************************************************************************
Explanation
The exact format of each of the tables mentioned above is detailed in thedocumentation which follows.
Tables enclosed in brackets [], above, are not always required.
RCHRES -- Section HYDR Input
496
4.4(3).2.1 Table-type HYDR-PARM1 -- Flags for HYDR section
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HYDR-PARM1 <-range> <v><1><2><3> <---odfvfg----> <---odgtfg----> <----funct----> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-PARM1 ******* Example *******
HYDR-PARM1 RCHRES Flags for HYDR section*** # - # VC A1 A2 A3 ODFVFG for each *** ODGTFG for each FUNCT for each FG FG FG FG possible exit *** possible exit possible exit 1 7 0 1 1 1 0 0 0 0 1 1 1 1 1 1 3 3 3 3 3 END HYDR-PARM1
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<v> VCONFG I3 0 0 1 <1> AUX1FG I3 0 0 1 <2> AUX2FG I3 0 0 1 <3> AUX3FG I3 0 0 1 <odfvfg> ODFVFG(5) 5I3 0 -5 8 <odgtfg> ODGTFG(5) 5I3 0 0 5 <funct> FUNCT(5) 5I3 1 1 3 ----------------------------------------------------------
RCHRES -- Section HYDR Input
497
Explanation
A value of 1 for VCONFG means that F(vol) (volume-dependent) outflow demandcomponents are multiplied by a factor which is allowed to vary through the year.These monthly adjustment factors are input in Table-type MON-CONVF in this section.
A value of 1 for AUX1FG means a routine will be called to compute depth, stage,surface area, average depth, and top width, and values for these parameters willbe reported in the printout. These are used in the calculation of precipitationand evaporation fluxes, and simulation of most water quality sections. A value of0 suppresses the calculation and printout of this information.
A value of 1 for AUX2FG means average velocity and average cross sectional areawill be calculated, and values for these parameters will be reported in theprintout. These are used in the simulation of oxygen. A value of 0 suppresses thecalculation/printout of this information. If AUX2FG is 1, AUX1FG must also be 1.
A value of 1 for AUX3FG means the shear velocity and bed shear stress will becalculated. These are used in the calculation of deposition and scour of sediment(inorganic and organic). AUX3FG may only be turned ON (=1) if AUX1FG and AUX2FGare also 1.
The value specified for ODFVFG(I) determines the F(vol) component of the outflowdemand for exit I. A value of 0 means that the outflow demand does not have avolume dependent component. A value greater than 0 indicates the column number inthe FTABLE which contains the F(vol) component. If the value specified for ODFVFGis less than 0, the absolute value indicates the element of array COLIND() whichdefines a pair of columns in the FTABLE which are used to evaluate the F(vol)component. Further explanation of this latter option is provided in the functionaldescription of the HYDR section in Part E. A value of ODFVFG can be specified foreach exit from a RCHRES. (Note: COLIND is specified as a time series.)
The value specified for ODGTFG(I) determines the G(t) (time-dependent) componentof the outflow demand for exit I. A value of 0 means that the outflow demand doesnot have such a component. A value greater than 0 indicates the element (index)number of the array OUTDGT() (or array COTDGT() if Categories are being simulated)which contains the G(t) component. A value of ODGTFG can be specified for eachexit from a RCHRES. (Note: OUTDGT and COTDGT are specified in the form of timeseries.)
FUNCT determines the function used to combine the components of an outflow demand.The possible values and their meanings are:
1 means use the smaller of F(vol) and G(t) 2 means use the larger of F(vol) and G(t) 3 means use the sum of F(vol) and G(t)
RCHRES -- Section HYDR Input
498
4.4(3).2.2 Table-type HYDR-PARM2 -- Parameters for HYDR section
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HYDR-PARM2 <-range><--------------------hydr-parm2----------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-PARM2******* Example ******* HYDR-PARM2 RCHRES *** # - # DSN FTBN LEN DELTH STCOR KS*** DB50 1 17 2.7 120. 3.2 .5 0.2 2 100 2 1.5 60. 1. .5 0.2 END HYDR-PARM2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<hydr-parm2> FTBDSN F5.0 0 0 999 none Both FTABNO F5.0 none 1 999 none Both LEN F10.0 none 0.01 none miles Engl none 0.016 none km Metric DELTH F10.0 0.0 0.0 none ft Engl 0.0 0.0 none m Metric STCOR F10.0 0.0 none none ft Engl 0.0 none none m Metric KS F10.0 0.0 0.0 .99 none Both DB50 F10.0 .01 .0001 100. in Engl .25 .0025 2500. mm Metric--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
499
Explanation
FTBDSN is the WDM table data-set number containing the F-Table. If FTBDSN isgreater than zero, the system searches the WDM file for the F-Table.
If FTBDSN = 0, FTABNO is the ID number for the F-Table (located in the FTABLESBlock) which contains the geometric and hydraulic properties of the RCHRES. IfFTBDSN > 0, FTABNO is the WDM table indicator specifying which table (within theWDM table data set given by FTBDSN) contains the F-Table.
LEN is the length of the RCHRES.
DELTH is the drop in water elevation from the upstream to the downstreamextremities of the RCHRES. (It is used if section OXRX is active and reaerationis being computed using the Tsivoglou-Wallace equation; or if section SEDTRN isactive and sandload transport capacity is being computed using either the Toffaletior Colby method).
STCOR is the correction to the RCHRES depth to calculate stage. (Depth + STCOR = Stage)
KS is the weighting factor for hydraulic routing. Choice of a realistic KS valueis discussed in the functional description of the HYDR section in Part E.
DB50 is the median diameter of the bed sediment (assumed constant throughout therun). This value is used to:
1. Calculate the bed shear stress if the RCHRES is a lake.2. Calculate the rate of sand transport if the Colby or Toffaleti methods are
used.
Note: The value input for DB50 is also used in section SEDTRN as the sand particlediameter required for sandload computations.
RCHRES -- Section HYDR Input
500
4.4(3).2.3 Table-type MON-CONVF -- Monthly F(vol) adjustment factors
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MON-CONVF <-range><----------------mon-convf---------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-CONVF
******* Example *******
MON-CONVF RCHRES Monthly F(vol) adjustment factors*** # - # JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC*** 1 7 .97 .89 .89 .91 .93 .93 .94 .95 .95 .98 .98 .97 END MON-CONVF********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<mon-convf> CONVFM(12) 12F5.0 0.0 0.0 none----------------------------------------------------------
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section HYDR Input
501
4.4(3).2.4 Table-type HYDR-INIT -- Initial conditions for HYDR section
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HYDR-INIT <-range><--vol---> ct<-------colind----------> <-------outdgt----------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-INIT
*******Example*******
HYDR-INIT Initial conditions for HYDR section *** RCHRES VOL Cat Initial value of COLIND *** Initial value of OUTDGT # - # ac-ft for each possible exit *** for each possible exit <--------> <><---><---><---><---><---> *** <---><---><---><---><---> 1 12.050 UN 4.0 1.2 1.8 END HYDR-INIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<vol> VOL F10.0 0.0 0.0 none acre-ft Engl 0.0 0.0 none Mm3 Metric ct CAT A2 blank none none none Both<colind> COLIND(5) 5F5.0 4.0 4.0 8.0 none Both<outdgt> OUTDGT(5) 5F5.0 0.0 0.0 none ft3/s Engl 0.0 0.0 none m3/s Metric--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
502
Explanation
VOL is the initial volume of water in the RCHRES.
CAT may be either an integer or a category identifier (ID tag - defined in theCATEGORY block). If it is a tag, all initial volume belongs to that category. IfCAT is an integer, the initial volume is divided according to Table-type HYDR-CINIT, where CAT entries are expected. If CAT is zero or blank, the initial volumeis divided equally among all active categories. CAT is ignored if no CATEGORYblock is present in the UCI file.
The value of COLIND(I) for exit I indicates the pair of columns in the FTABLE thatare used to evaluate the initial value of the F(vol) (volume-dependent) componentof outflow demand for the exit.
The array OUTDGT(I) specifies the initial value of the outflow demand for exit I,i.e., the G(t) (time-dependent) component. It is ignored if a Category block ispresent in the UCI file. Initial values for COTDGT are found in Table-type HYDR-CDEMAND.
A non-zero value of COLIND(I) is only meaningful if the outflow from exit I has anF(vol) component. Similarly, a non-zero value for OUTDGT(I) is only meaningful ifthe outflow from exit I has a G(t) component.
RCHRES -- Section HYDR Input
503
4.4(3).2.5 Table-type HYDR-CATEGORY -- Categories associated with outflows and other fluxes
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HYDR-CATEGORY cpr cev cf1 cf2 cf3 cf4 cf5 ncgt <range> <> <> <> <> <> <> <> <> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-CATEGORY
*******Example*******
HYDR-CATEGORY Categories specified for Outflows, Precipitation and Evaporation *** RCHRES Prec Evap<----------FVOL--------->NCOGT *** # - # 1 2 3 4 5 *** <---><---><---><---><---><---><---><---> *** 1 UN 4 TX 2 3 END HYDR-CATEGORY
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------cpr CPREC A2 none none none none Bothcev CEVAP A2 none none none none Bothcf1-cf5 CFVOL(*) A2 none none none none Bothncgt NOCGT I5 0 0 20 none Both--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
504
Explanation
CPREC, CEVAP, CFVOL(*)This table may be used to specify the categories that are impacted by outflows andother fluxes. For the physical quantities precipitation, evaporation, and exit-specific F(vol) outflow, categories may be specified in one of two ways: 1) Asingle category is specified by its two-character tag, right-justified in thefield. 2) If more than one category must be specified, then the number ofcategories is placed in the field. Then the multiple categories are fullyspecified in Table-types HYDR-CPREC, HYDR-CEVAP, and HYDR-CFVOL. If the field isblank or zero, or the table is omitted, then water is added to or subtracted fromall categories in proportion to their current storage fraction.
NCOGT is the number of COTDGT time series specifying category-associated demandsfrom the reach. These time series are assigned priorities and initial values inTable-type HYDR-CDEMAND. The time series are input in the time series blocks(e.g., EXT SOURCES). (See the Time Series Catalog for RCHRES).
RCHRES -- Section HYDR Input
505
4.4(3).2.6 Table-type HYDR-CINIT -- Allocation of initial volumes to categories
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** HYDR-CINIT ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac <-range> <><----> <><----> <><----> <><----> <><----> <><----> <><----> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-CINIT
*******Example******* HYDR-CINIT*** Initial Category Storage Fractions*** RCHRES c cfrac c cfrac c cfrac c cfrac c cfrac c cfrac c cfrac*** # - # <><----> <><----> <><----> <><----> <><----> <><----> <><----> 1 UN 0.2 TX 0.1 CU 0.1 FG 0.1 Z1 0.1 MI 0.05 A0 0.1 1 BB 0.1 AA 0.05 BB 0.1 END HYDR-CINIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------ct CINIT(*) A2 none none none none Bothcfrac CFRAC(*) F6.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
Explanation
This table is required when CAT in Table-type HYDR-INIT is a positive integer. CATdefines the number of pairs to be specified in this table; up to seven pairs canbe specified on a line.
CINIT is a two-character category tag.
CFRAC is the fraction of the initial volume belonging to the correspondingcategory. The sum of all fractions must be 1.0; alternatively, if all CFRACS areblank or zero, the initial volume will be equally divided among all categories.
RCHRES -- Section HYDR Input
506
4.4(3).2.7 Table-type HYDR-CPREC -- Allocation of precipitation to categories
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HYDR-CPREC ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac <-range> <><----> <><----> <><----> <><----> <><----> <><----> <><----> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-CPREC
*******Example*******
HYDR-CPREC*** Category Fractions for precipitation*** RCHRES ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac ct cfrac*** # - # <><----> <><----> <><----> <><----> <><----> <><----> <><----> 1 UN 0.2 TX 0.1 CU 0.1 FG 0.1 Z1 0.1 MI 0.05 A0 0.1 1 BB 0.1 AA 0.05 BB 0.1 END HYDR-CPREC
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------ct CPRECC(*) A2 none none none none Bothcfrac CPRECF(*) F6.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
507
Explanation
This table is required when CPREC in Table-type HYDR-CATEGORY is a positiveinteger. CPREC defines the number of pairs to be specified in this table; up toseven pairs can be specified on a line.
CPRECC is a two-character category tag.
CPRECF is the fraction of the precipitation which will be assigned to thecorresponding category. The sum of all fractions must be 1.0; alternatively if allfractions are blank or zero, the precipitation will be equally divided among allthe categories listed.
RCHRES -- Section HYDR Input
508
4.4(3).2.8 Table-type HYDR-CEVAP -- Allocation of evaporation to categories
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HYDR-CEVAP ct pr frac ct pr frac ct pr frac ct pr frac ct pr frac <-range> <><-><----> <><-><----> <><-><----> <><-><----> <><-><----> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-CEVAP
*******Example*******
HYDR-CEVAP Category Fractions and Priorities for Evaporation *** RCHRES ct pr frac ct pr frac ct pr frac ct pr frac ct pr frac *** # - # <><-><----> <><-><----> <><-><----> <><-><----> <><-><----> *** 1 UN 1 CU 2 0.5 MI 2 0.5 TX 3 1.0 BB 4 1 AA 4 END HYDR-CEVAP
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------ct CEVAPC(*) A2 none none none none Bothpr CEVAPP(*) I3 0 0 none none Bothcfrac CEVAPF(*) F6.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
509
Explanation
This table is required when CEVAP in Table-type HYDR-CATEGORY is a positiveinteger. CEVAP defines the number of categories to be specified in this table; upto five categories can be specified on a line.
CEVAPC is a two-character category tag.
CEVAPP is an integer signifying the priority of the corresponding category whensubtracting evaporation. Water is taken from the lowest priority categories first,then from the next-lowest priority categories, and so on. Categories with zero orundefined priority are taken last.
CEVAPF is the fraction of the evaporation which will be assigned to thecorresponding category at a given priority level. The sum of all fractions at apriority level must be 1.0; alternatively, if they are all blank or zero, theevaporative loss will be equally divided among all the categories listed.
RCHRES -- Section HYDR Input
510
4.4(3).2.9 Table-type HYDR-CFVOL -- Allocation of volume-dependent outflow to categories
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HYDR-CFVOL ct x pr frac ct x pr frac ct x pr frac ct x pr frac <-range> <><><-><----> <><><-><----> <><><-><----> <><><-><----> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-CFVOL
*******Example*******
HYDR-CFVOL Category Fractions and Priorities for F(VOL) Outflow *** RCHRES ct x pr frac ct x pr frac ct x pr frac ct x pr frac *** # - # <><><-><----> <><><-><----> <><><-><----> <><><-><----> *** 1 UN 1 1 CU 1 2 0.5 MI 1 2 0.5 UN 2 1 0.5 1 BB 2 1 0.5 AA 2 2 END HYDR-CFVOL
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Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------ct CFVOLC(*) A2 none none none none Bothx CFVOLE(*) I2 0 0 5 none Bothpr CFVOLP(*) I3 0 0 none none Bothcfrac CFVOLF(*) F6.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
511
Explanation
This table is required when any member of CFVOL in Table-type HYDR-CATEGORY is apositive integer. The number of categories given in this table is the sum of theinteger CFVOLs in HYDR-CATEGORY; up to four can be specified on a line.
CFVOLC is a two-character category tag.
CFVOLE is the number of the exit for which the category is being specified.
CFVOLP is an integer signifying the priority of the corresponding category whensubtracting F(vol) (volume-dependent) outflow. Water is taken from the lowestpriority categories first, then from the next-lowest priority categories, and soon. Categories with zero or undefined priority are taken last.
CFVOLF is the fraction of the F(vol) outflow which will be assigned to thecorresponding category at a given priority level. The sum of all fractions at apriority level must sum to 1.0; alternatively, if all fractions for a prioritylevel are blank or zero, the outflow demand will be equally divided among all ofthose categories.
RCHRES -- Section HYDR Input
512
4.4(3).2.10 Table-type HYDR-CDEMAND -- Allocation of time-dependent outflow to categories
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HYDR-CDEMAND ct x py pm pd cotdgt ct x py pm pd cotdgt <-range> <><> <--> <> <> <----> <><> <--> <> <> <----> . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HYDR-CDEMAND
*******Example*******
HYDR-CDEMAND Category Priorities and Initial Values for G(T) Demands *** ct x Priority COTDGT ct x Priority COTDGT *** RCHRES (yyyy/mm/dd) (cfs) (yyyy/mm/dd) (cfs) *** # - # <><> <--> <> <> <----> <><> <--> <> <> <----> 1 UN 1 1865 50.0 MI 1 1900/01 30.0 1 CU 2 1900/05/01 25.0 END HYDR-CDEMAND
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------ct COGTC(*) A2 none none none none Bothx COGTE(*) I2 0 0 5 none Bothpy COGTPY(*) I4 0 0 none none Bothpm COGTPM(*) I2 1 1 12 none Bothpd COGTPD(*) I2 1 1 31 none Bothcotdgt COTDGT(*) F6.0 0.0 0.0 none none Both--------------------------------------------------------------------------------
RCHRES -- Section HYDR Input
513
Explanation
This table is used in conjunction with the time-series COTDGT to specify outflowdemands which are a function of time, and to allocate these outflow demands amongcategories. The table may be omitted when NCOGT in Table-type HYDR-CATEGORY iszero.
COGTC is a two-character category tag.
COGTE is the exit number for the demand being specified.
COGTPY (year), COGTPM (month), and COGTPD (day) signify the priority date of thecorresponding demand timeseries. Multiple demands on the same category aresatisfied from earliest to latest priority date. Unspecified (blank) prioritiesare satisfied last.
4.4(3).3 RCHRES BLOCK -- Section ADCALC input
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[Table-type ADCALC-DATA]
********************************************************************************
Explanation
The exact format of this input is detailed below. Table ADCALC-DATA is not alwaysrequired because its contents can be defaulted.
RCHRES -- Section ADCALC Input
514
4.4(3).3.1 Table-type ADCALC-DATA -- Data for section ADCALC
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ADCALC-DATA <-range><---adcalc-data----> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END ADCALC-DATA
******* Example *******
ADCALC-DATA RCHRES Data for section ADCALC *** # - # CRRAT VOL *** 5 1.7 324. END ADCALC-DATA
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<adcalc-data> CRRAT 2F10.0 1.5 1.0 none none Both VOL 0.0 0.0 none acre-ft Engl 0.0 0.0 none Mm3 Metric--------------------------------------------------------------------------------
Explanation
Section ADCALC must be active if any of the following sections are active.
CRRAT is the ratio of maximum velocity to mean velocity in the RCHRES cross-sectionunder typical flow conditions.
VOL is the volume of water in the RCHRES at the start of the simulation. Input ofthis value is not necessary if section HYDR is active. (Note: Metric units are10**6 m3).
RCHRES -- Section CONS Input
515
4.4(3).4 RCHRES BLOCK -- Section CONS input
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[Table-type NCONS] [Table-type CONS-AD-FLAGS] --- Table-type CONS-DATA | repeat for each conservative constituent ---
********************************************************************************
Explanation
The exact formats of these tables are detailed below. Table-type NCONS is notrequired if only one conservative constituent is being simulated (default value).
RCHRES -- Section CONS Input
516
4.4(3).4.1 Table-type NCONS -- Number of conservative constituents simulated
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NCONS <-range><ncn> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END NCONS
******* Example *******
NCONS RCHRES *** # - #NCONS *** 1 7 4 END NCONS
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<ncn> NCONS I5 1 1 10----------------------------------------------------------
Explanation
NCONS is the number of conservative constituents being simulated.
RCHRES -- Section CONS Input
517
4.4(3).4.2 Table-type CONS-AD-FLAGS -- Atmospheric deposition flags for CONS ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** CONS-AD-FLAGS <-range> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END CONS-AD-FLAGS ******* Example ******* CONS-AD-FLAGS RCHRES Atmospheric deposition flags *** *** CONS1 CONS2 CONS3 CONS4 CONS5 CONS6 CONS7 CONS8 CONS9 QAL10 #*** # <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 13 -1 10 0 0 11 0 -1 0 0 -1 0 END CONS-AD-FLAGS ********************************************************************************
Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> COADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation COADFG is an array of flags indicating the source of atmospheric deposition datafor the CONS section. Each CONS has two flags. The first is for dry or totaldeposition flux, and the second is for wet deposition concentration. The flagvalues indicate: 0 No deposition of this type is simulated -1 Deposition of this type is input as time series COADFX or COADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number.
RCHRES -- Section CONS Input
518
4.4(3).4.3 Table-type CONS-DATA -- Information about one conservative substance ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** CONS-DATA <-range><----conid---------><---con--> <concid><--conv--> <qtyid-> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END CONS-DATA ******* Example ******* CONS-DATA RCHRES Data for conservative constituent No. 3 *** # - # Substance-id Conc ID CONV QTYID *** 1 7 Total Diss Solids 251.3 mg/l 1000. kg END CONS-DATA
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<conid> CONID(5) 5A4 blank none none none Both<con> CON F10.0 0.0 0.0 none concid Both<concid> CONCID 2A4 blank none none none Both<conv> CONV F10.0 none 1.0E-30 none see below <qtyid> QTYID 2A4 blank none none none Both--------------------------------------------------------------------------------
RCHRES -- Section CONS Input
519
Explanation
Any string of up to 20 characters may be supplied as the name of the conservativeconstituent (CONID).
CON is the initial concentration of the conservative constituent.
CONCID is a string of up to 8 characters which specifies the concentration unitsfor the conservative constituent. If the constituent provides the alkalinity timeseries for section PHCARB, CONCID must be mg/l as CaCO3.
QTYID is a string of up to 8 characters which specifies the units in which thetotal flow of constituent into, or out of, the RCHRES will be expressed, e.g.,"kg".
CONV is the conversion factor from QTYID/VOL to the desired concentration units(CONCID): CONC = CONV*(QTYID/VOL). If English units are being used (EMFG = 1 inthe GLOBAL Block), VOL is in ft3; if Metric units are in effect (EMFG = 2), VOL isin m3. For example, if: CONCID is mg/l QTYID is kg VOL is in m3,
then CONV=1000.
RCHRES -- Section HTRCH Input
520
4.4(3).5 RCHRES BLOCK -- Section HTRCH input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type HT-BED-FLAGS] [Table-type HEAT-PARM] --- [Table-type HT-BED-PARM] | if BEDFLG = 1 or 2 [Table-type MON-HT-TGRND] if TGFLG = 3 | --- --- [Table-type HT-BED-DELH] | if BEDFLG = 3 [Table-type HT-BED-DELTT] | --- [Table-type HEAT-INIT]
********************************************************************************
Explanation
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
RCHRES -- Section HTRCH Input
521
4.4(3).5.1 Table-type HT-BED-FLAGS -- Flags for bed conduction in section HTRCH
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
HT-BED-FLAGS <-range><bfg><gfg><tst> . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . END HT-BED-FLAGS
******* Example ******* HT-BED-FLAGS RCHRES *** # - # BDFG TGFG TSTP *** 1 3 55 2 2 2 END HT-BED-FLAGS
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max name(s) --------------------------------------------------------------------------------<bfg> BEDFLG I5 0 0 3<gfg> TGFLG I5 2 1 3<tst> TSTOP I5 55 1 100--------------------------------------------------------------------------------
Explanation
BEDFLG is the bed conduction flag, with the following meanings:0 - bed conduction is not simulated1 - single interface (water-mud) heat transfer method2 - two-interface (water-mud and mud-ground) heat transfer method3 - Jobson method
TGFLG specifies the source of the ground temperature for the bed conduction; usedwhen BEDFLG is 1 or 2 (TGFLG: 1=time series; 2=single value; 3=monthly values).
TSTOP is the number of time steps (prior to the current time step) that impact theheat flux at the current time step; used only when the Jobson method is in effect.
RCHRES -- Section HTRCH Input
522
4.4(3).5.2 Table-type HEAT-PARM -- Parameters for section HTRCH
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
HEAT-PARM <-range><--elev--><--eldat-><--cfsx--><--ktrd--><--kcnd--><--kevp--> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HEAT-PARM
******* Example ******* HEAT-PARM RCHRES ELEV ELDAT CFSAEX KATRAD KCOND KEVAP *** # - # ft ft *** 1 7 2000. 1500. .5 6.5 11. 4. END HEAT-PARM ********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<elev> ELEV F10.0 0.0 0.0 30000. ft Engl 0.0 0.0 10000. m Metric<eldat> ELDAT F10.0 0.0 none none ft Engl 0.0 none none m Metric<cfsx> CFSAEX F10.0 1.0 0.001 2.0 none Both<ktrd> KATRAD F10.0 9.37 1.00 20. none Both<kcnd> KCOND F10.0 6.12 1.00 20. none Both<kevp> KEVAP F10.0 2.24 1.00 10. none Both--------------------------------------------------------------------------------
Explanation
ELEV is the mean RCHRES elevation.ELDAT is the difference in elevation between the RCHRES and the air temperature gage (positive if RCHRES is higher than the gage).CFSAEX is the correction factor for solar radiation; it is the fraction of theRCHRES surface exposed to radiation.KATRAD is the longwave radiation coefficient.KCOND is the conduction-convection heat transport coefficient.KEVAP is the evaporation coefficient.
RCHRES -- Section HTRCH Input
523
4.4(3).5.3 Table-type HT-BED-PARM -- Bed conduction parameters for section HTRCH
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** HT-BED-PARM <-range><-muddep-><--tgrnd-><--kmud--><-kgrnd--> . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . END HT-BED-PARM
******* Example ******* HT-BED-PARM RCHRES MUDDEP TGRND KMUD KGRND *** # - # m deg C (kcal/m2/C/hr) *** 1 7 0.1 20. 50.4 1.42 END HT-BED-PARM ********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<-muddep-> MUDDEP F10.0 0.33 0.01 none ft Engl 0.1 0.01 none m Metric<--tgrnd-> TGRND F10.0 59. 14. 113. deg F Engl 15. -10. 45. deg C Metric<--kmud--> KMUD F10.0 50. 0.0 none kcal/m2/C/hr Both<--kgrnd-> KGRND F10.0 1.4 0.0 none kcal/m2/C/hr Both--------------------------------------------------------------------------------
Explanation
MUDDEP is the depth of the mud layer in the two-interface model (BEDFLG = 2).
TGRND is the constant (TGFLG = 2) ground temperature; it is used in the one andtwo-interface models (BEDFLG = 1 or 2). Optionally, the ground temperature can beinput in the form of twelve monthly values or a time series.
KMUD is the heat conduction coefficient between water and the mud/ground; it isused if BEDFLG = 1 or 2. Typical values range from 3 to 100 kcal/m2/C/hr.
KGRND is the heat conduction coefficient between ground and mud in the two-interface model (BEDFLG = 2).
RCHRES -- Section HTRCH Input
524
4.4(3).5.4 Table-type MON-HT-TGRND -- Monthly ground temperatures for bed heat conduction algorithms
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-HT-TGRND <-range><-----------------------12-values--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-HT-TGRND
******* Example *******
MON-HT-TGRND RCHRES TG1 TG2 TG3 TG4 TG5 TG6 TG7 TG8 TG9 TG10 TG11 TG12*** # - # *** 1 7 15. 16. 17. 18. 19. 20. 20. 20. 20. 18. 17. 16. END MON-HT-TGRND
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> TGRNDM(1-12) F5.0 none 14. 113. deg F English none -10. 45 deg C Metric--------------------------------------------------------------------------------
Explanation
TGRNDM(1) through TGRNDM(12) are monthly ground temperatures for use in the bedheat conduction models. This table must be included in the UCI only if TGFLG isassigned a value of 3 and BEDFLG = 1 or 2 in Table-type HT-BED-FLAGS.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section HTRCH Input
525
4.4(3).5.5 Table-type HT-BED-DELH -- Heat fluxes for Jobson bed heat conduction method
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
HT-BED-DELH <-range><--delh1-><--delh2-><--delh3-><--delh4-><--delh5-><--delh6-><--delh7-> . . <-range><-delh99-><-delh100> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HT-BED-DELH
******* Example ******* HT-BED-DELH RCHRES *** DELH DELH DELH DELH DELH DELH DELH # - # *** 1 2 3 4 5 6 7 1 -14.2 -9.44 -7.80 -6.66 -5.77 -5.34 -4.99 1 -4.71 -4.47 -4.27 -3.81 -3.93 -3.53 -3.66 etc END HT-BED-DELH
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<--delhi-> DELH(100) F10.0 0.0 none none btu/ft2/F/ivl English 0.0 none none kcal/m2/C/ivl Metric--------------------------------------------------------------------------------Explanation
DELH are bed sediment-to-water heat fluxes (per one degree increase in watertemperature) for the past TSTOP time intervals; used in the Jobson bed-conductionmethod. A maximum of 100 values of DELH are possible.
Caution: DELH values are dependent on the time step (INDELT in GLOBAL Block).
RCHRES -- Section HTRCH Input
526
4.4(3).5.6 Table-type HT-BED-DELTT -- Initial temperature changes for Jobson bed conduction method
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
HT-BED-DELTT <-range><-deltt1-><-deltt2-><-deltt3-><-deltt4-><-deltt5-><-deltt6-><-deltt7-> . . <-range><deltt99-><deltt100> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END HT-BED-DELTT
******* Example ******* HT-BED-DELTT RCHRES *** DELTT DELTT DELTT DELTT DELTT DELTT DELTT # - # *** 1 2 3 4 5 6 7 *** 8 9 10 11 12 13 14 1 14.2 9.44 7.80 6.66 5.77 5.34 4.99 1 4.71 4.47 4.27 3.81 3.93 3.53 3.66 etc END HT-BED-DELTT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<-deltti-> DELTT(100) F10.0 0.0 none none deg F English 0.0 none none deg C Metric--------------------------------------------------------------------------------Explanation
DELTT are initial water temperature changes for the TSTOP time intervalsimmediately preceding the starting time of the simulation; used in the Jobson bed-conduction method. A maximum of 100 values of DELTT are possible. DELTT valuesare positive if the water temperature increases.
RCHRES -- Section HTRCH Input
527
4.4(3).5.7 Table-type HEAT-INIT -- Initial conditions for HTRCH
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
HEAT-INIT <-range><----init-temp-----> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END HEAT-INIT
******* Example ******* HEAT-INIT RCHRES TW AIRTMP *** # - # degF degF *** 1 7 62. 70. END HEAT-INIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<init-temp> TW F10.0 60. 32. 200. degF Engl 15.5 0.0 95. degC Metric AIRTMP F10.0 60. -90. 150. degF Engl 15.5 -70.0 65. degC Metric--------------------------------------------------------------------------------
Explanation
TW is the initial water temperature and AIRTMP indicates the initial airtemperature at the RCHRES.
RCHRES -- Section SEDTRN Input
528
4.4(3).6 RCHRES-BLOCK -- Section SEDTRN input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
[Table-type SANDFG] Table-type SED-GENPARM Table-type SED-HYDPARM -- only if Section HYDR is inactive Table-type SAND-PM Table-type SILT-CLAY-PM -- include twice, 1st for silt, 2nd for clay [Table-type SSED-INIT] [Table-type BED-INIT]
********************************************************************************
Explanation
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
RCHRES -- Section SEDTRN Input
529
4.4(3).6.1 Table-type SANDFG -- Sandload method flag
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SANDFG <-range><sfg> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END SANDFG ******* Example *******
SANDFG RCHRES *** # - # SDFG *** 2 2 END SANDFG
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<sfg> SANDFG I5 3 1 3 ----------------------------------------------------------
Explanation
SANDFG indicates the method that will be used for sandload simulation: 1 = Toffaleti method 2 = Colby method 3 = user-specified power function method.
RCHRES -- Section SEDTRN Input
530
4.4(3).6.2 Table-type SED-GENPARM -- General sediment related parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SED-GENPARM <-range><----------gen-parm----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END SED-GENPARM
******* Example *******
SED-GENPARM RCHRES BEDWID BEDWRN POR*** # - # (m) (m) *** 3 10 30. 2. 0.4 END SED-GENPARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<gen-parm> BEDWID F10.0 none 1.0 none ft Engl none 0.3 none m Metric BEDWRN F10.0 100. .001 none ft Engl 30.5 .0003 none m Metric POR F10.0 0.5 0.1 0.9 none Both--------------------------------------------------------------------------------Explanation
BEDWID is the width of the cross-section over which HSPF will assume bed sediment is deposited (regardless of stage, top-width, etc); BEDWID is constant.It is used to estimate the depth of bed sediment.
BEDWRN is the bed depth which, if exceeded (e.g., through deposition) will causea warning message to be printed in the echo file (MESSU). POR is the porosity of the bed (volume voids/total volume). It is used to estimatebed depth.
RCHRES -- Section SEDTRN Input
531
4.4(3).6.3 Table-type SED-HYDPARM -- Parameters normally read in Section HYDR
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SED-HYDPARM <-range><--------sed-hydparm---------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END SED-HYDPARM
******* Example *******
SED-HYDPARM RCHRES LEN DELTH DB50*** # - # (km) (m) (mm)*** 2 5.0 4.0 0.5 5 20.0 5.0 0.3 END SED-HYDPARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sed-hydparm> LEN F10.0 none 0.01 none miles Engl none 0.016 none km Metric DELTH F10.0 0.0 0.0 none ft Engl 0.0 0.0 none m Metric DB50 F10.0 .01 .0001 100. in Engl .25 .0025 2500. mm Metric--------------------------------------------------------------------------------
Explanation
This table is only required and read if Section HYDR is not active. Normally theseparameters are supplied in Table-type HYDR-PARM2; see section HYDR for definitions.
RCHRES -- Section SEDTRN Input
532
4.4(3).6.4 Table-type SAND-PM -- Parameters related to sand transport
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SAND-PM <-range><------------------sand-parms--------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SAND-PM*******Example******* SAND-PM RCHRES D W *** # - # (in) (in/sec) *** 3 .01 1.2 END SAND-PM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sand-parms> D F10.0 none .001 100. in Engl none .025 2500. mm Metric W F10.0 none .02 500. in/sec Engl none .5 12500. mm/sec Metric RHO F10.0 2.65 1.0 4.0 gm/cm3 Both KSAND F10.0 0.0 0.0 none complex Both EXPSND F10.0 0.0 0.0 none complex Both--------------------------------------------------------------------------------
Explanation
D is the effective diameter of the transported sand particles, and W is thecorresponding fall velocity in still water. Note: the sand transport algorithmsdo not actually use D; they use DB50, supplied in Table-type HYDR-PARM2. D isincluded here for consistency with the input data supplied for cohesive sediment.
RHO is the density of the sand particles.
KSAND and EXPSND are the coefficient and exponent in the sandload power functionformula. These values should be input if SANDFG=3.
RCHRES -- Section SEDTRN Input
533
4.4(3).6.5 Table-type SILT-CLAY-PM -- Parameters for silt or clay
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SILT-CLAY-PM <-range><----------------------silt-clay-pm------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END SILT-CLAY-PM
******* Example *******
SILT-CLAY-PM RCHRES D W RHO TAUCD TAUCS M *** # - # (mm) (mm/sec) (gm/cm3) (kg/m2) (kg/m2) (kg/m2.d) *** 6 .03 .80 2.7 2.0 2.5 0.1 9 .04 1.5 2.6 2.0 3.0 .08 END SILT-CLAY-PM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<silt-clay-pm> D F10.0 0.0 0.0 .003 in Engl 0.0 0.0 .07 mm Metric W F10.0 0.0 0.0 .2 in/sec Engl 0.0 0.0 5.0 mm/sec Metric RHO F10.0 2.65 2.0 4.0 gm/cm3 Both TAUCD F10.0 1.0E10 1.0E-10 none lb/ft2 Engl 1.0E10 1.0E-10 none kg/m2 Metric TAUCS F10.0 1.0E10 1.0E-10 none lb/ft2 Engl 1.0E10 1.0E-10 none kg/m2 Metric M F10.0 0.0 0.0 none lb/ft2.d Engl 0.0 0.0 none kg/m2.d Metric--------------------------------------------------------------------------------
RCHRES -- Section SEDTRN Input
534
Explanation
This table must be supplied twice; first for silt, then for clay.
D is the effective diameter of the particles and W is the corresponding fallvelocity in still water.
RHO is the density of the particles.
TAUCD is the critical bed shear stress for deposition. Above this stress, therewill be no deposition; as the stress drops below this value to zero, depositionwill gradually increase to the value implied by the fall velocity in still water. TAUCS is the critical bed shear stress for scour. Below this value, there will beno scour; above it, scour will steadily increase.
In general TAUCD should be less than or equal to TAUCS.
M is the erodibility coefficient of the sediment.
Note that the default values for W, TAUCD, TAUCS, and M have been set so that siltand clay will behave as "washload"; that is, material will settle at the rateimplied by W (defaulted to zero) and there will be no scour; the material willbehave like a conservative substance.
RCHRES -- Section SEDTRN Input
535
4.4(3).6.6 Table-type SSED-INIT -- Initial concentrations of suspended sediment
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SSED-INIT <-range><---------ssed-init----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END SSED-INIT *******Example*******
SSED-INIT RCHRES Suspended sed concs (mg/l) *** # - # Sand Silt Clay *** 1 5 100. 50. 20. END SSED-INIT
************************************************* *******************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ssed-init> SSED(3) 3F10.0 0.0 0.0 none mg/l Both--------------------------------------------------------------------------------
Explanation
The three values supplied are the initial concentrations of suspended sand, silt,and clay, respectively.
RCHRES -- Section SEDTRN Input
536
4.4(3).6.7 Table-type BED-INIT -- Initial content of bed sediment
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
BED-INIT <-range><-bed-dep><fracsand><fracsilt><fracclay> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END BED-INIT
******* Example *******
BED-INIT RCHRES BEDDEP Initial bed composition *** # - # (m) Sand Silt Clay *** 3 1.5 0.6 0.2 0.2 END BED-INIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<bed-dep> BEDDEP F10.0 0.0 0.0 none ft Engl 0.0 0.0 none m Metric<fracsand> temporary F10.0 1.0 .0001 1.0 none Both<fracsilt> array F10.0 0.0 0.0 .9999 none Both<fracclay> F10.0 0.0 0.0 .9999 none Both--------------------------------------------------------------------------------
Explanation
BEDDEP is the initial total depth (thickness) of the bed.
The three values supplied under <fracsand>, <fracsilt>, and <fracclay> are theinitial fractions (by weight) of sand, silt, and clay in the bed material. Thedefault values are arranged to simulate an all-sand bed. The sum of the fractionsmust be 1.00.
RCHRES -- Section GQUAL Input
537
4.4(3).7 RCHRES-BLOCK -- Section GQUAL input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
[Table-type GQ-GENDATA] [Table-type GQ-AD-FLAGS]
next 15 tables -- repeat for each qual Table-type GQ-QALDATA [Table-type GQ-QALFG] [Table-type GQ-FLG2] Table-type GQ-HYDPM -- only if qual undergoes hydrolysis (QALFG(1,I)=1) Table-type GQ-ROXPM -- only if qual undergoes oxidation (QALFG(2,I)=1) [Table-type GQ-PHOTPM] -- only if qual undergoes photolysis (QALFG(3,I)=1) Table-type GQ-CFGAS -- only if qual undergoes volatilization (QALFG(4,I)=1)
next 2 tables -- only if qual undergoes biodegradation (QALFG(5,I)=1) Table-type GQ-BIOPM Table-type MON-BIO -- only if biomass is input monthly (GQPM2(7,I)=3) Table-type GQ-GENDECAY -- only if qual has "general" decay (QALFG(6,I)=1)
next 5 tables -- only if qual is sediment associated (QALFG(7,I)=1) [Table-type GQ-SEDDECAY] Table-type GQ-KD Table-type GQ-ADRATE [Table-type GQ-ADTHETA] [Table-type GQ-SEDCONC]
[Table-type GQ-VALUES]
next 3 tables -- only if the data are to be read as monthly values (Source flag in Table-type GQ-GENDATA is ON) [Table-type MON-WATEMP] [Table-type MON-PHVAL] -- only if there is hydrolysis (any QALFG(1)=1) [Table-type MON-ROXYGEN] -- only if there is oxidation (any QALFG(2)=1)
next 8 tables -- only if there is photolysis (any QALFG(3) = 1) Table-type GQ-ALPHA [Table-type GQ-GAMMA] [Table-type GQ-DELTA] [Table-type GQ-CLDFACT]
RCHRES -- Section GQUAL Input
538
next 3 tables -- only if the data are to be read as monthly values (Source flag in Table-type GQ-GENDATA is ON) [Table-type MON-CLOUD] [Table-type MON-SEDCONC] [Table-type MON-PHYTO] [Table-type SURF-EXPOSED] -- only if Section HTRCH is inactive (see Section PLANK for documentation)
next 7 tables -- only if there is volatilization (any QALFG(4) = 1) [Table-type OX-FLAGS] [Table-type ELEV] [Table-type OX-CFOREA] [Table-type OX-TSIVOGLOU] Table-type OX-LEN-DELTH [Table-type OX-TCGINV] Table-type OX-REAPARM [Table-type GQ-DAUGHTER] -- repeat for each decay process that produces daughter quals from parents
******************************************************************************** Explanation
A "qual" is a generalized quality constituent simulated using this module section.
The exact format of each of the tables above, except those "borrowed" from SectionsOXRX and PLANK, is detailed in the documentation which follows. Tables in brackets[] need not always be supplied; for example, because all of the inputs have defaultvalues.
RCHRES -- Section GQUAL Input
539
4.4(3).7.1 Table-type GQ-GENDATA -- General input for Section GQUAL
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-GENDATA <-range><ngq><--------source-fgs----------><lat> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END GQ-GENDATA
******* Example *******
GQ-GENDATA RCHRES NGQL TPFG PHFG ROFG CDFG SDFG PYFG LAT*** # - # *** 1 7 3 2 2 1 2 2 3 48 END GQ-GENDATA
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ngq> NGQUAL I5 1 1 3 none Both<source-fgs> TEMPFG I5 2 1 3 none Both PHFLAG I5 2 1 3 none Both ROXFG I5 2 1 3 none Both CLDFG I5 2 1 3 none Both SDFG I5 2 1 3 none Both PHYTFG I5 2 1 3 none Both<lat> LAT I5 0 -54 54 degrees Both--------------------------------------------------------------------------------
RCHRES -- Section GQUAL Input
540
Explanation
NGQUAL - number of generalized constituents (quals) being simulated. TEMPFG - source of water temperature data. 1 means a time series - either input or computed; 2 means a single user-supplied value; 3 means 12 user- supplied values (one for each month).PHFLAG - source of pH data. Input only if any QALFG(1)=1. Source designation scheme is the same as for TEMPFG.ROXFG - source of free radical oxygen data. Input only if any QALFG(2)=1. Source designation scheme is the same as for TEMPFG.CLDFG - source of cloud cover data. Input only if any QALFG(3)=1. Source designation scheme is the same as for TEMPFG.SDFG - source of total sediment concentration data. Input only if any QALFG(3)=1. Source designation scheme is the same as for TEMPFG. PHYTFG - source of phytoplankton data. Input only if any QALFG(3)=1. Source designation scheme is the same as for TEMPFG.LAT - latitude of the RCHRES. Input only if any QALFG(3)=1. Positive for northern hemisphere.
RCHRES -- Section GQUAL Input
541
4.4(3).7.2 Table-type GQ-AD-FLAGS -- Atmospheric deposition flags for GQUAL
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GQ-AD-FLAGS <-range> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-AD-FLAGS ******* Example ******* GQ-AD-FLAGS RCHRES Atmospheric deposition flags *** *** GQUAL1 GQUAL2 GQUAL3 #*** # <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 END GQ-AD-FLAGS
******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> GQADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation
GQADFG is an array of flags indicating the source of atmospheric deposition data.Each GQUAL has two flags. The first is for dry or total deposition flux, and thesecond is for wet deposition concentration. The flag values indicate:
0 No deposition of this type is simulated -1 Deposition of this type is input as time series GQADFX or GQADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number.
RCHRES -- Section GQUAL Input
542
4.4(3).7.3 Table-type GQ-QALDATA -- Data for a generalized quality constituent
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-QALDATA <-range><-------gqid-------><--dqal--> <cu><--conv--> <qtyid-> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-QALDATA
******* Example *******
GQ-QALDATA RCHRES *** GQID DQAL CONCID CONV QTYID # - # *** 1 7 Coliforms 2.0 # .001 # END GQ-QALDATA
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<gqid> GQID 5A4 none none none none Both<dqal> DQAL F10.0 0.0 0.0 none concid Both<cu> CONCID A4 none none none none Both<conv> CONV F10.0 none 1.0E-30 none see below <qtyid> QTYID 2A4 none none none none Both--------------------------------------------------------------------------------
Explanation
GQID - Name of constituent (qual).DQAL - Initial dissolved concentration of qual. CONCID - Concentration units (implied that it is "per liter") eg."mg"(/l).QTYID - Name of mass quantity unit for qual.CONV - Factor to convert from QTYID/VOL to concentration (CONCID) units: Conc= CONV* QTYID/VOL (in the English system, VOL is in ft3) (in the Metric system, VOL is in m3).
RCHRES -- Section GQUAL Input
543
4.4(3).7.4 Table-type GQ-QALFG -- First set of flags for a qual
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-QALFG <-range><---------degrad-fgs---------><sfg> . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . END GQ-QALFG
******* Example *******
GQ-QALFG RCHRES HDRL OXID PHOT VOLT BIOD GEN SDAS*** # - # *** 1 7 1 1 0 0 1 0 1 END GQ-QALFG********************************************************************************Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<degrad-fgs> QALFG(1) I5 0 0 1 none Both QALFG(2) I5 0 0 1 none Both QALFG(3) I5 0 0 1 none Both QALFG(4) I5 0 0 1 none Both QALFG(5) I5 0 0 1 none Both QALFG(6) I5 0 0 1 none Both<sfg> QALFG(7) I5 0 0 1 none Both--------------------------------------------------------------------------------Explanation
QALFG(1) - indicates whether hydrolysis is considered for dissolved qual. QALFG(2) - indicates whether oxidation by free radical oxygen is considered for dissolved qual.QALFG(3) - indicates whether photolysis is considered for dissolved qual. QALFG(4) - indicates whether volatilization is considered for dissolved qual. QALFG(5) - indicates whether biodegradation is considered for dissolved qual. QALFG(6) - indicates whether general first order decay is considered for dissolved qual.QALFG(7) - indicates whether or not qual is associated with sediment. If so, adsorption/desorption of qual is simulated.
RCHRES -- Section GQUAL Input
544
4.4(3).7.5 Table-type GQ-FLG2 -- Second set of flags for a qual
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-FLG2 <-range><-------daughter proc--------><sbm> . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . END GQ-FLG2
******* Example *******
GQ-FLG2 RCHRES HDRL OXID PHOT VOLT BIOD GEN SBMS*** # - # *** 1 7 0 0 1 0 1 0 2 END GQ-FLG2
********************************************************************************
Details --------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<daughter proc>GQPM2(1) I5 0 0 1 none Both GQPM2(2) I5 0 0 1 none Both GQPM2(3) I5 0 0 1 none Both GQPM2(4) I5 0 0 1 none Both GQPM2(5) I5 0 0 1 none Both GQPM2(6) I5 0 0 1 none Both<sbm> GQPM2(7) I5 2 1 3 none Both--------------------------------------------------------------------------------
Explanation
GQPM2(1) through GQPM2(6) indicate whether or not this qual is a daughter product through each of the six decay processes (1-hydrolysis, 2-oxidation,3-photolysis, 4-reserved for future use, 5-biodegradation, 6-general first orderdecay). GQPM2(7) indicates the source of biomass data for qual. Input only ifQALFG(5)=1. (1=time series, 2=single value, 3=twelve monthly values)
RCHRES -- Section GQUAL Input
545
4.4(3).7.6 Table-type GQ-HYDPM -- Hydrolysis parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-HYDPM <-range><-----------hydrol-parms---------------> . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . END GQ-HYDPM
******* Example *******
GQ-HYDPM RCHRES KA KB KN THHYD*** # - # *** 1 7 5000. 50. .00004 1.03 END GQ-HYDPM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<hydrol-parms> KA F10.0 none 1.0E-30 none /M-sec Both KB F10.0 none 1.0E-30 none /M-sec Both KN F10.0 none 1.0E-30 none /sec Both THHYD F10.0 1.0 1.0 2.0 none Both--------------------------------------------------------------------------------
Explanation
KA - second-order acid rate constant for hydrolysis KB - second-order base rate constant for hydrolysis KN - first-order rate constant of neutral reaction with water THHYD - temperature correction coefficient for hydrolysis
RCHRES -- Section GQUAL Input
546
4.4(3).7.7 Table-type GQ-ROXPM -- Parameters for free radical oxidation of qual
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-ROXPM <-range><------rox-pm------> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END GQ-ROXPM
******* Example *******
GQ-ROXPM RCHRES KOX THOX*** # - # *** 1 7 .000014 1.01 END GQ-ROXPM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<rox-pm> KOX F10.0 none 1.0E-30 none /M.sec Both THOX F10.0 1.0 1.0 2.0 none Both-------------------------------------------------------------------------------- Explanation KOX - second-order rate constant for oxidation by free radical oxygenTHOX - temperature correction coefficient for oxidation by free radical oxygen
RCHRES -- Section GQUAL Input
547
4.4(3).7.8 Table-type GQ-PHOTPM -- Parameters for photolysis
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-PHOTPM <-range><--------------------------first-7-----------------------------------> <-range><--------------------------second-7----------------------------------> <-range><---------------last-4-----------------><--phi---><-theta--> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-PHOTPM
******* Example *******
GQ-PHOTPM # - #*** K1 K2 K3 K4 K5 K6 K7 # - #*** K8 K9 K10 K11 K12 K13 K14 # - #*** K15 K16 K17 K18 PHI THETA 1 7 .5 .5 .5 .5 .5 .5 .5 1 7 .5 .5 .5 .5 .5 .5 .5 1 7 .5 .5 .5 .5 .47 1.02 END GQ-PHOTPM ********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<first-7> PHOTPM(1-7) F10.0 0.0 0.0 none l/M.cm Both<second-7> PHOTPM(8-14) F10.0 0.0 0.0 none l/M.cm Both<last-4> PHOTPM(15-18) F10.0 0.0 0.0 none l/M.cm Both<phi> PHOTPM(19) F10.0 1.0 .0001 10.0 M/E Both<theta> PHOTPM(20) F10.0 1.0 1.0 2.0 none Both--------------------------------------------------------------------------------
RCHRES -- Section GQUAL Input
548
Explanation
PHOTPM(1) through PHOTPM(18) are molar absorption coefficients for qual for 18 wavelength ranges of light (see functional description for subroutine DDECAY inPart E).
PHOTPM(19) is the quantum yield for the qual in air-saturated pure water.
PHOTPM(20) is the temperature correction coefficient for photolysis.
When an entry has to be continued onto more than 1 line:
1. No blank or comment lines may be put between any of the lines for a continuedentry. Put all comments ahead of the entry. (See above example).
2. The <range> specification must be repeated for each line onto which the entryis continued.
RCHRES -- Section GQUAL Input
549
4.4(3).7.9 Table-type GQ-CFGAS -- Ratio of volatilization rate to oxygen reaeration rate
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-CFGAS <-range><--cfgas-> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END GQ-CFGAS
******* Example *******
GQ-CFGAS RCHRES CFGAS*** # - # *** 1 7 .70 END GQ-CFGAS
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<cfgas> CFGAS F10.0 none 1.0E-30 none none Both--------------------------------------------------------------------------------
Explanation
CFGAS is the ratio of the volatilization rate to the oxygen reaeration rate.
RCHRES -- Section GQUAL Input
550
4.4(3).7.10 Table-type GQ-BIOPM -- Biodegradation parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-BIOPM <-range><----------bioparm-----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END GQ-BIOPM
******* Example *******
GQ-BIOPM RCHRES BIOCON THBIO BIO*** # - # *** 1 7 .31 1.07 .04 END GQ-BIOPM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<bioparm> BIOCON F10.0 none 1.0E-30 none l/mg/day Both THBIO F10.0 1.07 1.0 2.0 none Both BIO F10.0 none 0.00001 none mg/l Both-------------------------------------------------------------------------------- Explanation BIOCON - second order rate constant for biodegradation of qual by biomassTHBIO - temperature correction coefficient for biodegradation of qualBIO - concentration of the biomass which causes biodegradation of qual
RCHRES -- Section GQUAL Input
551
4.4(3).7.11 Table-type MON-BIO -- Monthly values of biomass
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-BIO <-range><-----------------------12-values--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-BIO
******* Example *******
MON-BIO RCHRES BM1 BM2 BM3 BM4 BM5 BM6 BM7 BM8 BM9 BM10 BM11 BM12*** # - # *** 1 7 .03 .035 .03 .02 .02 .03 .03 .035 .040 .060 .050 .035 END MON-BIO
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> BIOM(1-12) F5.0 none 0.00001 none mg/l Both--------------------------------------------------------------------------------
Explanation
BIOM(1) through BIOM(12) are monthly concentrations of biomass that causebiodegradation of qual. This table must be included in the UCI only if GQPM2(7)is assigned a value of 3 in Table-type GQ-FLG2.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
552
4.4(3).7.12 Table-type GQ-GENDECAY -- Parameters for "general" decay of qual
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-GENDECAY <-range><----decay-pms-----> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END GQ-GENDECAY
******* Example *******
GQ-GENDECAY RCHRES FSTDEC THFST*** # - # *** 1 7 0.2 1.05 END GQ-GENDECAY
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<decay-pms> FSTDEC F10.0 none .00001 none /day Both THFST F10.0 1.07 1.0 2.0 none Both--------------------------------------------------------------------------------
Explanation
FSTDEC - first-order decay rate for qualTHFST - temperature correction coefficient for first-order decay of qual
RCHRES -- Section GQUAL Input
553
4.4(3).7.13 Table-type GQ-SEDDECAY -- Parameters for decay of contaminant adsorbed to sediment
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-SEDDECAY <-range><--------------ads-decay---------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END GQ-SEDDECAY
*******Example*******
GQ-SEDDECAY RCHRES KSUSP THSUSP KBED THBED*** # - # *** 1 7 .01 1.06 .005 1.03 END GQ-SEDDECAY
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ads-decay> ADDCPM(1) F10.0 0.0 0.0 none /day Both ADDCPM(2) F10.0 1.07 1.0 2.0 none Both ADDCPM(3) F10.0 0.0 0.0 none /day Both ADDCPM(4) F10.0 1.07 1.0 2.0 none Both--------------------------------------------------------------------------------
Explanation
ADDCPM(1) - decay rate for qual adsorbed to suspended sedimentADDCPM(2) - temperature correction coefficient for decay of qual on suspended sedimentADDCPM(3) - decay rate for qual adsorbed to bed sedimentADDCPM(4) - temperature correction coefficient for decay of qual on bed sediment
RCHRES -- Section GQUAL Input
554
4.4(3).7.14 Table-type GQ-KD -- Adsorption coefficients of qual
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-KD <-range><------------------------k-part----------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-KD
******* Example *******
GQ-KD RCHRES ADPM1 ADPM2 ADPM3 ADPM4 ADPM5 ADPM6*** # - # *** 1 7 1.0 5000 15000 .3 1000 4000 END GQ-KD
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<k-part> ADPM(1,1) F10.0 none 1.0E-10 none l/mg Both ADPM(2,1) F10.0 none 1.0E-10 none l/mg Both ADPM(3,1) F10.0 none 1.0E-10 none l/mg Both ADPM(4,1) F10.0 none 1.0E-10 none l/mg Both ADPM(5,1) F10.0 none 1.0E-10 none l/mg Both ADPM(6,1) F10.0 none 1.0E-10 none l/mg Both--------------------------------------------------------------------------------
Explanation
ADPM(1,1) through ADPM(6,1) are distribution coefficients for qual with:1-suspended sand, 2-suspended silt, 3-suspended clay, 4-bed sand, 5-bed silt, and6-bed clay.
RCHRES -- Section GQUAL Input
555
4.4(3).7.15 Table-type GQ-ADRATE -- Adsorption/desorption rate parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-ADRATE <-range><-------------------------k-adsdes-------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-ADRATE
*******Example *******
GQ-ADRATE RCHRES ADPM1 ADPM2 ADPM3 ADPM4 ADPM5 ADPM6*** # - # *** 1 7 400. 400. 400. .0028 .0028 .0028 END GQ-ADRATE
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<k-adsdes> ADPM(1,2) F10.0 none .00001 none /day Both ADPM(2,2) F10.0 none .00001 none /day Both ADPM(3,2) F10.0 none .00001 none /day Both ADPM(4,2) F10.0 none .00001 none /day Both ADPM(5,2) F10.0 none .00001 none /day Both ADPM(6,2) F10.0 none .00001 none /day Both--------------------------------------------------------------------------------
Explanation
ADPM(1,2) through ADPM(6,2) are transfer rates between adsorbed and desorbed statesof qual with: 1-suspended sand, 2-suspended silt, 3-suspended clay, 4-bed sand,5-bed silt, and 6-bed clay.
RCHRES -- Section GQUAL Input
556
4.4(3).7.16 Table-type GQ-ADTHETA-- Adsorption/desorption temperature correction parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GQ-ADTHETA <-range><----------------------thet-adsdes-------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-ADTHETA
******* Example *******
GQ-ADTHETA RCHRES ADPM1 ADPM2 ADPM3 ADPM4 ADPM5 ADPM6*** # - # *** 1 7 1.07 1.07 1.07 1.04 1.04 1.04 END GQ-ADTHETA ********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<thet-adsdes> ADPM(1,3) F10.0 1.07 1.0 2.0 none Both ADPM(2,3) F10.0 1.07 1.0 2.0 none Both ADPM(3,3) F10.0 1.07 1.0 2.0 none Both ADPM(4,3) F10.0 1.07 1.0 2.0 none Both ADPM(5,3) F10.0 1.07 1.0 2.0 none Both ADPM(6,3) F10.0 1.07 1.0 2.0 none Both-------------------------------------------------------------------------------- Explanation ADPM(1,3) through ADPM(6,3) are temperature correction coefficients foradsorption/desorption on: 1-suspended sand, 2-suspended silt, 3-suspended clay,4-bed sand, 5-bed silt, 6-bed clay.
RCHRES -- Section GQUAL Input
557
4.4(3).7.17 Table-type GQ-SEDCONC -- Initial concentrations on sediment
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-SEDCONC <-range><------------------------sedconc---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-SEDCONC
******* Example *******
GQ-SEDCONC RCHRES SQAL1 SQAL2 SQAL3 SQAL4 SQAL5 SQAL6*** # - # *** 1 7 1.3 8.4 8.9 1.9 8.4 9.2 END GQ-SEDCONC
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<sedconc> SQAL(1-6) F10.0 0.0 0.0 none concu/mg Both--------------------------------------------------------------------------------
Explanation
SQAL(1) through SQAL(6) are initial concentrations of qual on: 1-suspended sand,2-suspended silt, 3-suspended clay, 4-bed sand, 5-bed silt, 6-bed clay.
RCHRES -- Section GQUAL Input
558
4.4(3).7.18 Table-type GQ-VALUES -- Initial values for inputs which are constant
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-VALUES <-range><--twat--><-phval--><---roc--><---cld--><--sdcnc-><--phy---> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-VALUES
******* Example *******
GQ-VALUES RCHRES TWAT PHVAL ROC CLD SDCNC PHY*** # - # *** 1 7 22. 7. .07 1. 11. .007 END GQ-VALUES
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<twat> TWAT F10.0 60. 32. 212. deg F Engl 15.5 0.1 100. deg C Metric<phval> PHVAL F10.0 7.0 1.0 14. none Both<roc> ROC F10.0 0.0 0.0 none mole/l Both<cld> CLD F10.0 0.0 0.0 10. tenths Both<sdcnc> SDCNC F10.0 0.0 0.0 none mg/l Both<phy> PHY F10.0 0.0 0.0 none mg/l Both--------------------------------------------------------------------------------
RCHRES -- Section GQUAL Input
559
Explanation In Table-type GQ-GENDATA, values for data source flags are specified. If any ofthe flags are assigned a value of 2, a single constant value for that data typemust be provided in this table. For example, if ROXFG=2 a value for free radicaloxygen concentration (ROC) must be supplied in columns 31-40 of this table.
TWAT - water temperaturePHVAL - pHROC - free radical oxygen concentrationCLD - cloud coverSDCNC - total suspended sediment concentrationPHY - phytoplankton concentration (as biomass)
RCHRES -- Section GQUAL Input
560
4.4(3).7.19 Table-type MON-WATEMP -- Monthly values of water temperature
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-WATEMP <-range><-----------------------12-values--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-WATEMP
******* Example *******
MON-WATEMP RCHRES T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12*** # - # *** 1 7 34 37 39 42 55 59 64 62 58 54 46 38 END MON-WATEMP
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> TEMPM(1-12) F5.0 60. 32. 212. degF Engl 15.5 0.1 100. degC Metric------------------------------------------------------------------------------
Explanation
In Table-type GQ-GENDATA, values for data source flags are specified. If TEMPFGis assigned a value of 3, 12 monthly values for water temperature must be suppliedin this table.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
561
4.4(3).7.20 Table-type MON-PHVAL -- Monthly values of pH
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-PHVAL <-range><-----------------------12-values--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-PHVAL
*******Example*******
MON-PHVAL RCHRES PH1 PH2 PH3 PH4 PH5 PH6 PH7 PH8 PH9 PH10 PH11 PH12*** # - # *** 1 7 6.8 6.8 6.4 6.1 5.9 5.6 5.6 5.9 6.1 6.4 6.8 6.8 END MON-PHVAL
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> PHVALM(1-12) F5.0 7.0 1.0 14.0 none Both--------------------------------------------------------------------------------
Explanation
In Table-type GQ-GENDATA, values for data source flags are specified. If PHFLAGis assigned a value of 3, 12 monthly values for pH must be supplied in this table.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
562
4.4(3).7.21 Table-type MON-ROXYGEN -- Monthly values of free radical oxygen
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-ROXYGEN <-range><-----------------------12-values--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-ROXYGEN
*******Example*******
MON-ROXYGEN RCHRES OX1 OX2 OX3 OX4 OX5 OX6 OX7 OX8 OX9 OX10 OX11 OX12*** # - # *** 1 7 .09 .09 .10 .11 .12 .12 .12 .12 .12 .10 .09 .09 END MON-ROXYGEN
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> ROCM(1-12) F5.0 0.0 0.0 none mole/l Both--------------------------------------------------------------------------------
Explanation
In Table-type GQ-GENDATA, values for data source flags are specified. If ROXFG isassigned a value of 3, 12 monthly values for free radical oxygen concentration mustbe supplied in this table.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
563
4.4(3).7.22 Table-type GQ-ALPHA -- Values of base absorbance coefficient
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-ALPHA <-range><--------------------------first-7-----------------------------------> <-range><--------------------------second-7----------------------------------> <-range><--------------last-4------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-ALPHA
*******Example*******
GQ-ALPHA RCHRES*** # - #*** K1 K2 K3 K4 K5 K6 K7 # - #*** K8 K9 K10 K11 K12 K13 K14 # - #*** K15 K16 K17 K18 1 7 .008 .009 .010 .011 .011 .011 .012 1 7 .013 .015 .016 .017 .018 .019 .020 1 7 .021 .022 .024 .024 END GQ-ALPHA ********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<first-7> ALPH(1-7) F10.0 none .00001 none /cm Both<second-7> ALPH(8-14) F10.0 none .00001 none /cm Both<last-4> ALPH(15-18) F10.0 none .00001 none /cm Both--------------------------------------------------------------------------------
RCHRES -- Section GQUAL Input
564
Explanation
ALPH(1) through ALPH(18) are base absorption coefficients for 18 wavelengths oflight passing through clear water.
This table is necessary only when a qual undergoes photolysis; i.e., when anyQALFG(3)=1 in Table-type GQ-QALFG.
When an entry has to be continued onto more than 1 line:
1. No blank or comment lines may be put between any of the lines for a continuedentry. Put all comments ahead of the entry. (See above example).
2. The <range> specification must be repeated for each line onto which the entryis continued.
RCHRES -- Section GQUAL Input
565
4.4(3).7.23 Table-type GQ-GAMMA -- Values of sediment absorbance coefficient
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-GAMMA <-range><--------------------------first-7-----------------------------------> <-range><--------------------------second-7----------------------------------> <-range><--------------last-4------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-GAMMA
******* Example *******
GQ-GAMMA RCHRES*** # - #*** K1 K2 K3 K4 K5 K6 K7 # - #*** K8 K9 K10 K11 K12 K13 K14 # - #*** K15 K16 K17 K18 1 4 .001 .001 .001 .001 .001 .001 .001 1 4 .001 .002 .002 .002 .002 .002 .002 1 4 .002 .002 .002 .002 END GQ-GAMMA********************************************************************************Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<first-7> GAMM(1-7) F10.0 0.0 0.0 none l/mg.cm Both<second-7> GAMM(8-14) F10.0 0.0 0.0 none l/mg.cm Both<last-4> GAMM(15-18) F10.0 0.0 0.0 none l/mg.cm Both--------------------------------------------------------------------------------Explanation
GAMM(1) through GAMM(18) are increments to the base absorbance coefficient(Table-type GQ-ALPHA) for light passing through sediment-laden water.
This is table necessary only when a qual undergoes photolysis; i.e., when anyQALFG(3)=1 in Table-type GQ-QALFG.
See rules for continuing an entry onto more than 1 line in Explanation forTable-type GQ-ALPHA.
RCHRES -- Section GQUAL Input
566
4.4(3).7.24 Table-type GQ-DELTA -- Values of phytoplankton absorbance coefficient
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GQ-DELTA <-range><--------------------------first-7-----------------------------------> <-range><--------------------------second-7----------------------------------> <-range><--------------last-4------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-DELTA*******Example******* GQ-DELTA RCHRES*** # - #*** K1 K2 K3 K4 K5 K6 K7 # - #*** K8 K9 K10 K11 K12 K13 K14 # - #*** K15 K16 K17 K18 1 4 .0007 .0007 .0007 .0007 .0007 .0007 .0007 1 4 .0007 .0007 .0007 .0007 .0007 .0007 .0007 1 4 .0007 .0007 .0007 .0007 END GQ-DELTA ********************************************************************************Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<first-7> DEL(1-7) F10.0 0.0 0.0 none l/mg.cm Both<second-7> DEL(8-14) F10.0 0.0 0.0 none l/mg.cm Both<last-4> DEL(15-18) F10.0 0.0 0.0 none l/mg.cm Both--------------------------------------------------------------------------------Explanation
DEL(1) through DEL(18) are increments to the base absorption coefficient (Table-type GQ-ALPHA) for light passing through plankton-laden water.
This table is necessary only when a qual undergoes photolysis; i.e., when anyQALFG(3)=1 in Table-type GQ-QALFG.
See rules for continuing an entry onto more than 1 line in Explanation forTable-type GQ-ALPHA.
RCHRES -- Section GQUAL Input
567
4.4(3).7.25 Table-type GQ-CLDFACT -- Light extinction efficiency of cloud cover
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GQ-CLDFACT <-range><--------------------------first-7-----------------------------------> <-range><--------------------------second-7----------------------------------> <-range><--------------last-4------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GQ-CLDFACT
******* Example *******
GQ-CLDFACT RCHRES*** # - #*** F1 F2 F3 F4 F5 F6 F7 # - #*** F8 F9 F10 F11 F12 F13 F14 # - #*** F15 F16 F17 F18 1 4 .10 .10 .10 .15 .15 .15 .15 1 4 .17 .17 .17 .17 .18 .19 .20 1 4 .21 .21 .21 .21 END GQ-CLDFACT********************************************************************************Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<first-7> KCLD(1-7) F10.0 0.0 0.0 1.0 none Both<second-7> KCLD(8-14) F10.0 0.0 0.0 1.0 none Both<last-4> KCLD(15-18) F10.0 0.0 0.0 1.0 none Both--------------------------------------------------------------------------------Explanation
KCLD(1) through KCLD(18) are values of light extinction efficiency of cloud coverfor each of 18 wavelengths.
This table is necessary only when a qual undergoes photolysis; i.e., when anyQALFG(3)=1 in Table-type GQ-QALFG.
See rules for continuing an entry onto more than 1 line in Explanation forTable-type GQ-ALPHA.
RCHRES -- Section GQUAL Input
568
4.4(3).7.26 Table-type MON-CLOUD -- Monthly values of cloud cover
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-CLOUD <-range><----------------------12-values---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-CLOUD
*******Example*******
MON-CLOUD RCHRES C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12*** # - # *** 1 7 3 3 4 3 2 1 1 1 0 1 1 2 END MON-CLOUD
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> CLDM(1-12) F5.0 0.0 0.0 10.0 tenths Both--------------------------------------------------------------------------------
Explanation
CLDM(1) through CLDM(12) are monthly values of average cloud cover. This tablemust be included in the UCI only if CLDFG=3 in Table-type GQ-GENDATA.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
569
4.4(3).7.27 Table-type MON-SEDCONC -- Monthly values of sediment concentration
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-SEDCONC <-range><----------------------12-values---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-SEDCONC
*******Example*******
MON-SEDCONC RCHRES SC1 SC2 SC3 SC4 SC5 SC6 SC7 SC8 SC9 SC10 SC11 SC12*** # - # *** 1 7 2. 4. 10. 120. 75. 10. 8. 8. 6. 6. 4. 4. END MON-SEDCONC
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> SDCNCM(1-12) F5.0 0.0 0.0 none mg/l Both--------------------------------------------------------------------------------
Explanation
SDCNCM(1) through SDCNCM(12) are monthly average suspended sediment concentrationvalues. This table must be included in the UCI only if SDFG=3 in Table-typeGQ-GENDATA.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
570
4.4(3).7.28 Table-type MON-PHYTO -- Monthly values of phytoplankton concentration
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
MON-PHYTO <-range><----------------------12-values---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END MON-PHYTO
******* Example *******
MON-PHYTO RCHRES P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12*** # - # *** 1 7 .01 .03 .03 .03 .04 .11 .33 .47 .31 .17 .15 .06 END MON-PHYTO
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<12-values> PHYM(1-12) F5.0 0.0 0.0 none mg/l Both--------------------------------------------------------------------------------
Explanation
PHYM(1) through PHYM(12) are monthly values of phytoplankton concentration. Thistable must be included in the UCI only if PHYTFG=3 in Table-type GQ-GENDATA.
Note: The input monthly values apply to the first day of the month, and values forintermediate days are obtained by interpolating between successive monthly values.
RCHRES -- Section GQUAL Input
571
4.4(3).7.29 Table-type GQ-DAUGHTER -- Relationship between parent and daughter quals
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GQ-DAUGHTER <-range><--zero--><2-from-1><3-from-1> <-range><--zero--><--zero--><3-from-2> <-range><--zero--><--zero--><--zero--> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END GQ-DAUGHTER******* Example ******* GQ-DAUGHTER RCHRES *** # - # ZERO 2F1 3F1*** # - # ZERO ZERO 3F2*** # - # ZERO ZERO ZERO*** 1 7 0.0 .36 .02 1 7 0.0 0.0 1.24 1 7 0.0 0.0 0.0 END GQ-DAUGHTER********************************************************************************Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<zero> 0.0<2-from-1> C(2,1) F10.0 0.0 0.0 none none Both<3-from-1> C(3,1) F10.0 0.0 0.0 none none Both<3-from-2> C(3,2) F10.0 0.0 0.0 none none Both--------------------------------------------------------------------------------Explanation
This table-type specifies the relationship between parent and daughter compounds.For example, variable C(2,1) indicates the amount of qual #2 which is produced bydecay of qual #1 through one of the decay processes. The table must be repeatedin sequence for each decay process that produces daughter quals from decay ofparent quals. The proper sequence is: 1-hydrolysis, 2-oxidation by free radicaloxygen, 3-photolysis, 4-(reserved for future use), 5-biodegradation, 6-generalfirst order decay. For example, if biodegradation is the only process of interest,five of these tables must be included in the UCI file, and the fifth one shouldcontain nonzero values.
RCHRES -- Section RQUAL Input
572
4.4(3).8 RCHRES-BLOCK -- Input for RQUAL sections
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** [Table-type BENTH-FLAG] [Table-type SCOUR-PARMS] Section OXRX input [Section NUTRX input] if NUTRX is active [Section PLANK input] if PLANK is active [Section PHCARB input] if PHCARB is active ********************************************************************************
Explanation
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
RCHRES -- Section RQUAL Input
573
4.4(3).8.01 Table-type BENTH-FLAG -- Benthic release flag
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
BENTH-FLAG <-range><ben> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END BENTH-FLAG
******* Example ******* BENTH-FLAG RCHRES BENF*** # - # *** 1 7 END BENTH-FLAG
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ben> BENRFG I5 0 0 1 none Both--------------------------------------------------------------------------------
Explanation
If BENRFG is 1, benthal influences are considered in the following sections.
RCHRES -- Section RQUAL Input
574
4.4(3).8.02 Table-type SCOUR-PARMS -- Benthal scour parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SCOUR-PARMS <-range><----scour-parms---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END SCOUR-PARMS
*******Example*******
SCOUR-PARMS RCHRES SCRVEL SCRMUL*** # - # ft/sec *** 1 7 15. 3. END SCOUR-PARMS
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<scour-parms> SCRVEL F10.0 10. .01 none ft/sec Engl 3.05 .01 none m/sec Metric SCRMUL F10.0 2.0 1.0 none none Both--------------------------------------------------------------------------------
Explanation
SCRVEL is the threshold velocity above which the effect of scouring on benthalrelease rates is considered.
SCRMUL is the multiplier by which benthal releases are increased during scouring.
RCHRES -- Section OXRX Input
575
4.4(3).8.1 RCHRES-BLOCK -- Section OXRX input ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
+[Table-type OX-FLAGS] Table-type OX-GENPARM+[Table-type ELEV] if section HTRCH is not active [Table-type OX-BENPARM] if BENRFG=1 (Table-type BENTH-FLAG)+[Table-type OX-CFOREA] if LKFG=1 (Table-type GEN-INFO) ---- --- if |+[Table-type OX-TSIVOGLOU] | REAMFG=1 |+ Table-type OX-LEN-DELTH if section HYDR inactive | (Tsivoglou) | if --- | LKFG=0+[Table-type OX-TCGINV] if REAMFG=2 (Owen/Churchill,etc.) |+ Table-type OX-REAPARM if REAMFG=3 | ---- [Table-type OX-INIT]
******************************************************************************** Note: If any of the tables marked "+" above were supplied in your input for
Section GQUAL, they must not be repeated here (these are the tables used to calculate the oxygen reaeration coefficient, which, under certainconditions, is also needed in Section GQUAL).
Explanation
The conditions under which data from the various tables are needed are indicatedabove. REAMFG is the reaeration method flag, defined in Table-type OX-FLAGS below.
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
RCHRES -- Section OXRX Input
576
4.4(3).8.1.1 Table-type OX-FLAGS -- Oxygen flags
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-FLAGS <-range><oxf> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END OX-FLAGS
******* Example *******
OX-FLAGS RCHRES REAM *** # - # *** 1 7 2 END OX-FLAGS ********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<oxf> REAMFG I5 2 1 3 none Both--------------------------------------------------------------------------------
Explanation
REAMFG indicates the method used to calculate the reaeration coefficient for free-flowing streams.
1 Tsivoglou method is used. 2 Owens, Churchill, or O'Connor-Dobbins method is used depending on velocity
and depth of water. 3 Coefficient is calculated as a power function of velocity and/or depth; user
inputs exponents for velocity and depth and an empirical constant (REAK).
RCHRES -- Section OXRX Input
577
4.4(3).8.1.2 Table-type OX-GENPARM -- General oxygen parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-GENPARM <-range><-------------ox-genparm---------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END OX-GENPARM
******* Example *******
OX-GENPARM RCHRES KBOD20 TCBOD KODSET SUPSAT*** # - # /hr ft/hr *** 1 7 0.1 1.06 8.0 1.2 END OX-GENPARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ox-genparm> KBOD20 F10.0 none 1.0E-30 none /hr Both TCBOD F10.0 1.075 1.0 2.0 none Both KODSET F10.0 0.0 0.0 none ft/hr Engl 0.0 0.0 none m/hr Metric SUPSAT F10.0 1.15 1.0 2.0 none Both--------------------------------------------------------------------------------
Explanation
KBOD20 is the unit BOD decay rate @ 20 degrees C.TCBOD is the temperature correction coefficient for BOD decay.KODSET is the rate of BOD settling.SUPSAT is the maximum allowable dissolved oxygen supersaturation (expressed as amultiple of the dissolved oxygen saturation concentration).
RCHRES -- Section OXRX Input
578
4.4(3).8.1.3 Table-type ELEV -- RCHRES elevation above sea level
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
ELEV <-range><--elev--> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END ELEV
******* Example *******
ELEV RCHRES ELEV*** # - # ft*** 1 7 2100. END ELEV
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<elev> ELEV F10.0 0.0 0.0 30000 ft Engl 0.0 0.0 10000 m Metric--------------------------------------------------------------------------------
Explanation
ELEV is the mean RCHRES elevation above sea level.
RCHRES -- Section OXRX Input
579
4.4(3).8.1.4 Table-type OX-BENPARM -- Oxygen benthic parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-BENPARM <-range><--------ox-benparm----------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END OX-BENPARM
Example ******* OX-BENPARM RCHRES BENOD TCBEN EXPOD BRBOD(1) BRBOD(2) EXPREL *** # - # mg/m2.hr mg/m2.hr mg/m2.hr *** 1 7 1.0 END OX-BENPARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ox-benparm> BENOD F10.0 0.0 0.0 none mg/m2.hr Both TCBEN F10.0 1.074 1.0 2.0 none Both EXPOD F10.0 1.22 0.1 none none Both BRBOD(1) F10.0 72. .0001 none mg/m2.hr Both BRBOD(2) F10.0 100. .0001 none mg/m2.hr Both EXPREL F10.0 2.82 0.1 none none Both--------------------------------------------------------------------------------
Explanation
This table is used if BENRFG = 1 in Table-type BENTH-FLAG (see RQUAL section).
BENOD is the benthal oxygen demand at 20 degrees C (with unlimited DOconcentration) (demand is, thus, proportional to the water temperature).?TCBEN is the temperature correction coefficient for benthal oxygen demand.EXPOD is the exponential factor in the dissolved oxygen term of the benthal oxygendemand equation. BRBOD(1) is the benthal release rate of BOD under aerobic conditions, and BRBOD(2)is the increment to benthal release of BOD under anaerobic conditions.EXPREL is the exponent in the DO term of the benthal BOD release equation.
RCHRES -- Section OXRX Input
580
4.4(3).8.1.5 Table-type OX-CFOREA -- Lake reaeration correction coefficient
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-CFOREA <-range><-cforea-> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END OX-CFOREA
******* Example *******
OX-CFOREA RCHRES CFOREA*** # - # *** 1 7 0.8 END OX-CFOREA
********************************************************************************
Details-----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)-----------------------------------------------------------<cforea> CFOREA F10.0 1.0 .001 10.------------------------------------------------------------
Explanation
CFOREA is a correction factor in the lake reaeration equation; it accounts for goodor poor circulation characteristics.
RCHRES -- Section OXRX Input
581
4.4(3).8.1.6 Table-type OX-TSIVOGLOU -- Parameters for the Tsivoglou calculation of reaeration rate
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-TSIVOGLOU <-range><---ox-tsivoglou---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END OX-TSIVOGLOU
******* Example *******
OX-TSIVOGLOU RCHRES REAKT TCGINV*** # - # /ft *** 1 7 .07 1.1 END OX-TSIVOGLOU
********************************************************************************
Details----------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------------------------<ox-tsivoglou> REAKT F10.0 0.08 0.001 1.0 /ft Both TCGINV F10.0 1.047 1.0 2.0 none Both----------------------------------------------------------------------------
Explanation
This table is required if REAMFG is 1.
REAKT is the empirical constant in Tsivoglou's equation for reaeration (escapecoefficient).
TCGINV is the temperature correction coefficient for surface gas invasion.
RCHRES -- Section OXRX Input
582
4.4(3).8.1.7 Table-type OX-LEN-DELTH -- Length and water elevation drop of reach
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-LEN-DELTH <-range><---ox-len-delth---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END OX-LEN-DELTH
******* Example *******
OX-LEN-DELTH RCHRES LEN DELTH*** # - # miles ft*** 1 7 10. 200. END OX-LEN-DELTH
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ox-len-delth> LEN F10.0 none .01 none miles Engl none .01 none km Metric DELTH F10.0 none 0.00001 none ft Engl none 0.00001 none m Metric--------------------------------------------------------------------------------
Explanation
This table is only relevant if HYDR is inactive and REAMFG = 1.
LEN is the length of the RCHRES.
DELTH is the (energy) drop of the RCHRES over its length.
RCHRES -- Section OXRX Input
583
4.4(3).8.1.8 Table-type OX-TCGINV -- Owen/Churchill/O'Connor-Dobbins data (temperature correction coefficient)
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-TCGINV <-range><-tcginv-> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END OX-TCGINV
******* Example *******
OX-TCGINV RCHRES TCGINV*** # - # *** 1 7 1.07 END OX-TCGINV
********************************************************************************
Details------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<tcginv> TCGINV F10.0 1.047 1.0 2.0 ------------------------------------------------------------
Explanation
This table is used when REAMFG = 2.
TCGINV is the temperature correction coefficient for surface gas invasion.
RCHRES -- Section OXRX Input
584
4.4(3).8.1.9 Table-type OX-REAPARM -- Parameters for user-supplied reaeration formula
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-REAPARM <-range><-------------ox-reaparm---------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END OX-REAPARM
*******Example*******
OX-REAPARM RCHRES TCGINV REAK EXPRED EXPREV*** # - # /hr *** 1 7 1.08 1.0 -2.0 0.7 END OX-REAPARM
********************************************************************************
Details----------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system----------------------------------------------------------------------------<ox-reaparm> TCGINV F10.0 1.047 1.0 2.0 none Both REAK F10.0 none 1.0E-30 none /hr Both EXPRED F10.0 0.0 none 0.0 none Both EXPREV F10.0 0.0 0.0 none none Both----------------------------------------------------------------------------
Explanation
This table is used when REAMFG = 3.
TCGINV - See explanation for Table-type OX-TSIVOGLOU.REAK is the empirical constant in the equation used to calculate the reaerationcoefficient.EXPRED is the exponent to depth and EXPREV is the exponent to velocity in thereaeration coefficient equation.
RCHRES -- Section OXRX Input
585
4.4(3).8.1.10 Table-type OX-INIT -- Initial concentrations for the OXRX section
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
OX-INIT <-range><---------ox-init------------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END OX-INIT
*******Example*******
OX-INIT RCHRES DOX BOD SATDO*** # - # mg/l mg/l mg/l*** 1 7 26. 17.2 43. END OX-INIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ox-init> DOX F10.0 0.0 0.0 20.0 mg/l Both BOD F10.0 0.0 0.0 none mg/l Both SATDO F10.0 10.0 0.1 20.0 mg/l Both--------------------------------------------------------------------------------
Explanation
DOX is the initial dissolved oxygen.BOD is the initial biochemical oxygen demand.SATDO is the initial dissolved oxygen saturation concentration.
RCHRES -- Section NUTRX input
586
4.4(3).8.2 RCHRES-BLOCK -- Section NUTRX input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
[Table-type NUT-FLAGS] [Table-type NUT-AD-FLAGS] [Table-type CONV-VAL1] [Table-type NUT-BENPARM] if BENRFG=1 in Table-type BENTH-FLAG Table-type NUT-NITDENIT [Table-type NUT-NH3VOLAT] if NH3 volatilization is simulated (TAMFG=1 and AMVFG=1 in Table-type NUT-FLAGS) [Table-type MON-PHVAL] if NH3 is simulated and monthly values of pH are being input (TAMFG=1 and PHFLAG=3 in Table-type NUT-FLAGS) see section GQUAL for documentation --- [Table-type NUT-BEDCONC] | if NH3 or PO4 adsorption is simulated [Table-type NUT-ADSPARM] |--- ((TAMFG=1 and ADNHFG=1) or [Table-type NUT-ADSINIT] | (PO4FG=1 and ADPOFG=1) in Table-type NUT-FLAGS) --- [Table-type NUT-DINIT]********************************************************************************
Explanation
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
BENRFG indicates whether or not benthal influences are considered. TAMFG indicateswhether or not ammonia is simulated, and ADNHFG indicates whether ammoniaadsorption is considered. PO4FG indicates whether or not ortho-phosphorus issimulated, and ADPOFG indicates whether PO4 adsorption is considered.
RCHRES -- Section NUTRX input
587
4.4(3).8.2.1 Table-type NUT-FLAGS -- Nutrient flags
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-FLAGS <-range><---------nut-flags--------------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END NUT-FLAGS
******* Example *******
NUT-FLAGS RCHRES TAM NO2 PO4 AMV DEN ADNH ADPO PHFL *** # - # *** 1 7 1 1 END NUT-FLAGS
********************************************************************************
Details-----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)-----------------------------------------------------------<nut-flags> TAMFG,NO2FG, 7I5 0 0 1 PO4FG,AMVFG, DENFG,ADNHFG, ADPOFG PHFLG I5 2 1 3-----------------------------------------------------------
Explanation
TAMFG - If on, total ammonia is simulated.NO2FG - If on, nitrite is simulated.PO4FG - If on, ortho-phosphorus is simulated.AMVFG - If on, ammonia volatilization is enabled.DENFG - If on, denitrification is enabled.ADNHFG - If on, NH4 adsorption is simulated.ADPOFG - If on, PO4 adsorption is simulated.PHFLAG - Source of pH data (1=time series, 2=constant, 3=monthly values).
RCHRES -- Section NUTRX input
588
4.4(3).8.2.2 Table-type NUT-AD-FLAGS -- Atmospheric deposition flags for nutrients
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** NUT-AD-FLAGS <-range> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-AD-FLAGS ******* Example ******* NUT-AD-FLAGS RCHRES Atmospheric deposition flags *** *** NO3 NH3 PO4 #*** # <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 END NUT-AD-FLAGS
******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> NUADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation
NUADFG is an array of flags indicating the source of atmospheric deposition data.Each nutrient has two flags. The first is for dry or total deposition flux, andthe second is for wet deposition concentration. The flag values indicate: 0 No deposition of this type is simulated -1 Deposition of this type is input as time series NUADFX or NUADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number.
RCHRES -- Section NUTRX input
589
4.4(3).8.2.3 Table-type CONV-VAL1 -- Conversion factors for nutrients
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** CONV-VAL1 <-range><------------conv-val1-----------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END CONV-VAL1
*******Example*******
CONV-VAL1 RCHRES CVBO CVBPC CVBPN BPCNTC*** # - # mg/mg mols/mol mols/mol *** 1 7 4.0 67. 33. 77. END CONV-VAL1
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<conv-val1> CVBO F10.0 1.98 1.0 5.0 mg/mg Both CVBPC F10.0 106. 50. 200. mols/mol Both CVBPN F10.0 16. 10. 50. mols/mol Both BPCNTC F10.0 49. 10. 100. none Both -------------------------------------------------------------------------------- Explanation
CVBO - Conversion from milligrams biomass to milligrams oxygen.CVBPC - Conversion from biomass expressed as phosphorus to carbon.CVBPN - Conversion from biomass expressed as phosphorus to nitrogen.BPCNTC - Percentage of biomass which is carbon (by weight).
RCHRES -- Section NUTRX input
590
4.4(3).8.2.4 Table-type NUT-BENPARM -- Nutrient benthic parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** NUT-BENPARM <-range><---------------nut-benparm----------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-BENPARM
*******Example******* NUT-BENPARM RCHRES BRTAM(1) BRTAM(2) BRPO4(1) BRPO4(2) ANAER*** # - # mg/m2.hr mg/m2.hr mg/m2.hr mg/m2.hr mg/l*** 1 7 10. 20. 1.0 4.0 .001 END NUT-BENPARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<nut-benparm> BRTAM(1) 5F10.0 0.0 0.0 none mg/m2.hr Both BRTAM(2) 0.0 0.0 none mg/m2.hr Both BRPO4(1) 0.0 0.0 none mg/m2.hr Both BRPO4(2) 0.0 0.0 none mg/m2.hr Both ANAER .005 .0001 1.0 mg/l Both--------------------------------------------------------------------------------
Explanation
This table is used if BENRFG = 1 in Table-type BENTH-FLAG (see RQUAL section).
BRTAM(1) and BRTAM(2) are the benthal release rates of ammonia under aerobic andanaerobic conditions, respectively.
BRPO4(1) and BRPO4(2) are the benthal release rates of ortho-phosphorus. Thesubscripts are the same as BRTAM.
ANAER is the concentration of dissolved oxygen below which anaerobic conditions areassumed to exist.
RCHRES -- Section NUTRX input
591
4.4(3).8.2.5 Table-type NUT-NITDENIT -- Nitrification and denitrification parameters.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-NITDENIT <-range><--------nut-nitdenit--------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-NITDENIT
Example *******
NUT-NITDENIT RCHRES KTAM20 KNO220 TCNIT KNO320 TCDEN DENOXT *** # - # /hr /hr /hr mg/l *** 1 7 .05 .05 1.1 .05 1.08 4.0 END NUT-NITDENIT
********************************************************************************
Details--------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units name(s)--------------------------------------------------------------------- <nut-nitdenit> KTAM20 6F10.0 none 0.001 none /hr KNO220 none 0.001 none /hr TCNIT 1.07 1.0 2.0 KNO320 none 0.001 none /hr TCDEN 1.07 1.0 2.0 DENOXT 2.00 0.0 none mg/l---------------------------------------------------------------------
Explanation
KTAM20 and KNO220 are the nitrification rates of ammonia and nitrite,respectively, at 20 degrees C.
KNO320 is the nitrate denitrification rate at 20 degrees C.
TCNIT and TCDEN are the temperature correction coefficients for nitrificationand denitrification, respectively.
DENOXT is the dissolved oxygen concentration threshold for denitrification.
RCHRES -- Section NUTRX input
592
4.4(3).8.2.6 Table-type NUT-NH3VOLAT -- Ammonia volatilization parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-NH3VOLAT <-range><---nut-nh3volat---> . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . END NUT-NH3VOLAT
******* Example *******
NUT-NH3VOLAT RCHRES EXPNVG EXPNVL *** # - # *** 5 6 0.6 0.8 END NUT-NH3VOLAT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<nut-nh3volat> EXPNVG F10.0 0.5 0.1 2.0 none Both EXPNVL F10.0 .6667 0.1 2.0 none Both--------------------------------------------------------------------------------
Explanation
EXPNVG is the exponent in the gas layer mass transfer coefficient equation forNH3 volatilization.
EXPNVL is the exponent in the liquid layer mass transfer coefficient equationfor NH3 volatilization.
RCHRES -- Section NUTRX input
593
4.4(3).8.2.7 Table-type NUT-BEDCONC -- Bed concentrations of adsorbed NH3 and PO4
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-BEDCONC <-range><--------nut-bedconc---------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-BEDCONC
******* Example *******
NUT-BEDCONC RCHRES Bed concentrations of NH4 & PO4 (mg/kg) *** # - # NH4-sand NH4-silt NH4-clay PO4-sand PO4-silt PO4-clay *** 2 3 0.01 0.02 0.03 0.10 0.20 0.30 END NUT-BEDCONC
********************************************************************************
Details--------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units name(s)--------------------------------------------------------------------- <nut-bedconc> BNH4(3) 3F10.0 0.0 0.0 none mg/kg BPO4(3) 3F10.0 0.0 0.0 none mg/kg---------------------------------------------------------------------
Explanation
BNH4(1-3) are the constant bed concentrations of ammonia-N adsorbed to sand, silt,and clay.
BPO4(1-3) are the constant bed concentrations of ortho-phosphorus-P adsorbed tosand, silt, and clay.
RCHRES -- Section NUTRX input
594
4.4(3).8.2.8 Table-type NUT-ADSPARM -- Adsorption coefficients for ammonia and ortho-phosphorus
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-ADSPARM <-range><--------nut-adsparm---------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-ADSPARM
******* Example *******
NUT-ADSPARM RCHRES Partition coefficients for NH4 AND PO4 (cm3/g) *** # - # NH4-sand NH4-silt NH4-clay PO4-sand PO4-silt PO4-clay *** 2 3 0.10 0.30 0.50 0.10 0.50 0.80 END NUT-ADSPARM
********************************************************************************
Details--------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units name(s)--------------------------------------------------------------------- <nut-adsparm> ADNHPM(3) 3F10.0 1.E-10 1.E-10 none cm3/g ADPOPM(3) 3F10.0 1.E-10 1.E-10 none cm3/g ---------------------------------------------------------------------
Explanation
ADNHPM(1-3) are the adsorption coefficients (Kd) for ammonia-N adsorbed to sand,silt, and clay.
ADPOPM(1-3) are the adsorption coefficients for ortho-phosphorus-P adsorbed tosand, silt, and clay.
RCHRES -- Section NUTRX input
595
4.4(3).8.2.9 Table-type NUT-DINIT -- Initial concentrations of dissolved nutrients
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-DINIT <-range><--------nut-dinit-------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-DINIT
******* Example *******
NUT-DINIT RCHRES NO3 TAM NO2 PO4 PHVAL *** # - # mg/l mg/l mg/l mg/l ph units *** 1 3 1.0 0.3 0.01 0.02 7. END NUT-DINIT
********************************************************************************
Details--------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units name(s)--------------------------------------------------------------------- <nut-dinit> NO3 5F10.0 0.0 0.0 none mg/l TAM 0.0 0.0 none mg/l NO2 0.0 0.0 none mg/l PO4 0.0 0.0 none mg/l PHVAL 7.0 0.0 14.0 pH units---------------------------------------------------------------------
Explanation
NO3, TAM, and NO2 are the initial concentrations of nitrate, total ammonia, andnitrite (as N). PO4 is the initial concentration of ortho-phosphorus (as P).
PHVAL is the constant pH value if PHFLG=2, and the initial value of pH if PHFLG=1or 3.
RCHRES -- Section NUTRX input
596
4.4(3).8.2.10 Table-type NUT-ADSINIT -- Initial concentrations of NH3 and PO4 adsorbed to suspended sediment
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
NUT-ADSINIT <-range><--------nut-adsinit---------------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END NUT-ADSINIT
******* Example *******
NUT-ADSINIT RCHRES Initial suspended NH4 and PO4 concentrations (mg/kg) *** # - # NH4-sand NH4-silt NH4-clay PO4-sand PO4-silt PO4-clay *** 2 3 0.10 0.30 0.50 0.10 0.50 0.80 END NUT-ADSINIT
********************************************************************************
Details--------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units name(s)--------------------------------------------------------------------- <nut-adsinit> SNH4(3) 3F10.0 0.0 0.0 none mg/kg SPO4(3) 3F10.0 0.0 0.0 none mg/kg---------------------------------------------------------------------
Explanation
SNH4(1-3) are the initial concentrations of ammonia-N adsorbed to sand, silt, andclay.
SPO4(1-3) are the initial concentrations of ortho-phosphorus-P adsorbed to sand,silt, and clay.
RCHRES -- Section PLANK Input
597
4.4(3).8.3 RCHRES-BLOCK -- Section PLANK input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
Table-type PLNK-FLAGS [Table-type PLK-AD-FLAGS] Table-type SURF-EXPOSED if section HTRCH is inactive Table-type PLNK-PARM1 [Table-type PLNK-PARM2] [Table-type PLNK-PARM3] ----- Table-type PHYTO-PARM | --- | Table-type ZOO-PARM1 | if | if [Table-type ZOO-PARM2] | ZOOFG=1 | PHYFG=1 --- | ----- [Table-type BENAL-PARM] if BALFG=1 [Table-type PLNK-INIT]
********************************************************************************
Explanation
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
PHYFG, ZOOFG and BALFG are flags which indicate whether or not phytoplankton,zooplankton, and benthic algae are being simulated. They are documented underTable-type PLNK-FLAGS below.
RCHRES -- Section PLANK Input
598
4.4(3).8.3.1 Table-type PLNK-FLAGS -- Plankton flags
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PLNK-FLAGS <-range><-------------plnk-flags---------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END PLNK-FLAGS
******* Example ******* PLNK-FLAGS RCHRES PHYF ZOOF BALF SDLT AMRF DECF NSFG ZFOO *** # - # *** 1 7 1 1 3 END PLNK-FLAGS
********************************************************************************
Details------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<plnk-flags> PHYFG,ZOOFG, 7I5 0 0 1 BALFG,SDLTFG, AMRFG,DECFG, NSFG ZFOOD I5 2 1 3 ------------------------------------------------------------
Explanation
The following, except for ZFOOD, are the conditions when the flag is on:
PHYFG - Phytoplankton are simulated.ZOOFG - Zooplankton are simulated.BALFG - Benthic algae are simulated.SDLTFG - Influence of sediment washload on light extinction is simulated.AMRFG - Ammonia retardation of nitrogen-limited growth is enabled.DECFG - Linkage between carbon dioxide and phytoplankton growth is decoupled.NSFG - Ammonia is included as part of available nitrogen supply in nitrogen limited growth calculations.ZFOOD - Indicates the quality of zooplankton food.
RCHRES -- Section PLANK Input
599
4.4(3).8.3.2 Table-type PLNK-AD-FLAGS -- Atmospheric deposition flags for refractory organics
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PLK-AD-FLAGS <-range> <f><c> <f><c> <f><c> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PLK-AD-FLAGS ******* Example ******* PLK-AD-FLAGS RCHRES Atmospheric deposition flags *** *** ORN ORP ORC #*** # <F><C> <F><C> <F><C> 1 7 -1 10 -1 -1 11 12 END PLK-AD-FLAGS
******************************************************************************** Details ------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<f><c> PLADFG(*) (1X,2I3) 0 -1 none------------------------------------------------------------
Explanation
PLADFG is an array of flags indicating the source of atmospheric deposition data.Each organic constituent has two flags. The first is for dry or total depositionflux, and the second is for wet deposition concentration. The flag valuesindicate: 0 No deposition of this type is simulated -1 Deposition of this type is input as time series PLADFX or PLADCN >0 Deposition of this type is input in the MONTH-DATA table with the corresponding table ID number.
RCHRES -- Section PLANK Input
600
4.4(3).8.3.3 Table-type SURF-EXPOSED -- Correction factor for solar radiation data
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SURF-EXPOSED <-range><surf-exp> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END SURF-EXPOSED
******* Example *******
SURF-EXPOSED RCHRES CFSAEX *** # - # *** 1 7 .5 END SURF-EXPOSED
********************************************************************************
Details--------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system --------------------------------------------------------------------------<surf-exp> CFSAEX F10.0 1.0 0.001 1.0 none Both --------------------------------------------------------------------------
Explanation
This factor is used to adjust the input solar radiation to make it applicable tothe RCHRES; for example, to account for shading of the surface by trees orbuildings.
RCHRES -- Section PLANK Input
601
4.4(3).8.3.4 Table-type PLNK-PARM1 -- First group of general plankton parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PLNK-PARM1 <-range><---------------------plnk-parm1---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PLNK-PARM1
Example******* PLNK-PARM1 RCHRES RATCLP NONREF LITSED ALNPR EXTB MALGR*** # - # /ft /hr*** 1 7 .5 .3 .4 0.1 END PLNK-PARM1
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<plnk-parm1> RATCLP F10.0 .6 .01 none none Both NONREF F10.0 .5 .01 1.0 none Both LITSED F10.0 0.0 0.0 none l/mg.ft Both ALNPR F10.0 1.0 .01 1.0 none Both EXTB F10.0 none .001 none /ft Engl none .001 none /m Metric MALGR F10.0 .3 .001 none /hr Both--------------------------------------------------------------------------------
Explanation
RATCLP is the ratio of chlorophyll A content of biomass to phosphorus content.NONREF is the non-refractory fraction of algae and zooplankton biomass.LITSED is the multiplication factor to total sediment concentration to determinesediment contribution to light extinction.ALNPR is the fraction of nitrogen requirements for phytoplankton growth that issatisfied by nitrate.EXTB is the base extinction coefficient for light.MALGR is the maximum unit algal growth rate.
RCHRES -- Section PLANK Input
602
4.4(3).8.3.5 Table-type PLNK-PARM2 -- second group of general plankton parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PLNK-PARM2 <-range><--------------------------plnk-parm2--------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PLNK-PARM2
Example******* PLNK-PARM2 RCHRES *** CMMLT CMMN CMMNP CMMP TALGRH TALGRL TALGRM # - # ***ly/min mg/l mg/l mg/l degF degF degF 1 7 .01 .05 .04 85.0 44.0 71.0 END PLNK-PARM2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<plnk-parm2> CMMLT F10.0 .033 1.0E-6 none ly/min Both CMMN F10.0 .045 1.0E-6 none mg/l Both CMMNP F10.0 .0284 1.0E-6 none mg/l Both CMMP F10.0 .015 1.0E-6 none mg/l Both TALGRH F10.0 95. 50. 212. degF Engl 35. 10. 100. degC Metric TALGRL F10.0 43. -120. 212. degF Engl 6.1 -84. 100. degC Metric TALGRM F10.0 77. 32. 212. degF Engl 25. 0.0 100. degC Metric--------------------------------------------------------------------------------
Explanation
CMMLT is the Michaelis-Menten constant for light limited growth.CMMN is the nitrate Michaelis-Menten constant for nitrogen limited growth.CMMNP is the nitrate Michaelis-Menten constant for phosphorus limited growth.CMMP is the phosphate Michaelis-Menten constant for phosphorus limited growth.TALGRH is the temperature above which algal growth ceases.TALGRL is the temperature below which algal growth ceases.TALGRM is the temperature below which algal growth is retarded.
RCHRES -- Section PLANK Input
603
4.4(3).8.3.6 Table-type PLNK-PARM3 -- Third group of general plankton parameters ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PLNK-PARM3 <-range><---------------------plnk-parm3---------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PLNK-PARM3
******* Example ******* PLNK-PARM3 RCHRES ALR20 ALDH ALDL OXALD NALDH PALDH*** # - # /hr /hr /hr /hr mg/l mg/l*** 1 7 .02 .04 END PLNK-PARM3
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<plnk-parm3> ALR20 F10.0 .004 1.0E-6 none /hr Both ALDH F10.0 .01 1.0E-6 none /hr Both ALDL F10.0 .001 1.0E-6 none /hr Both OXALD F10.0 .03 1.0E-6 none /hr Both NALDH F10.0 0.0 0.0 none mg/l Both PALDH F10.0 0.0 0.0 none mg/l Both--------------------------------------------------------------------------------
Explanation
ALR20 is the algal unit respiration rate at 20 degrees C.ALDH is the high algal unit death rate.ALDL is the low algal unit death rate.OXALD is the increment to phytoplankton unit death rate due to anaerobicconditions.NALDH is the inorganic nitrogen concentration below which high algal death rateoccurs (as nitrogen).PALDH is the inorganic phosphorus concentration below which high algal death rateoccurs (as phosphorus).
RCHRES -- Section PLANK Input
604
4.4(3).8.3.7 Table-type PHYTO-PARM -- Phytoplankton parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PHYTO-PARM <-range><----------------------phyto-parm--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PHYTO-PARM
Example******* PHYTO-PARM RCHRES SEED MXSTAY OREF CLALDH PHYSET REFSET*** # - # mg/l mg/l ft3/s ug/l ft/hr ft/hr*** 1 7 2.0 15. 8.0 END PHYTO-PARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<phyto-parm> SEED F10.0 0.0 0.0 none mg/l Both MXSTAY F10.0 0.0 0.0 none mg/l Both OREF F10.0 0.0001 0.0001 none ft3/s Engl 0.0001 0.0001 none m3/s Metric CLALDH F10.0 50.0 .01 none ug/l Both PHYSET F10.0 0.0 0.0 none ft/hr Engl 0.0 0.0 none m/hr Metric REFSET F10.0 0.0 0.0 none ft/hr Engl 0.0 0.0 none m/hr Metric--------------------------------------------------------------------------------Explanation
SEED is the minimum concentration of plankton not subject to advection (i.e., athigh flow).MXSTAY is the concentration of plankton not subject to advection at very low flow.OREF is the outflow at which the concentration of plankton not subject to advectionis midway between SEED and MXSTAY.CLALDH is the chlorophyll A concentration above which high algal death rate occurs.PHYSET is the rate of phytoplankton settling.REFSET is the rate of settling for dead refractory organics.
RCHRES -- Section PLANK Input
605
4.4(3).8.3.8 Table-type ZOO-PARM1 -- First group of zooplankton parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
ZOO-PARM1 <-range><------------------zoo-parm1---------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END ZOO-PARM1
******* Example *******
ZOO-PARM1 RCHRES MZOEAT ZFIL20 ZRES20 ZD OXZD*** # - # mg/l.hr l/mgzoo.hr /hr /hr /hr*** 1 7 .098 0.2 END ZOO-PARM1
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<zoo-parm1> MZOEAT F10.0 .055 .001 none mg phyto/ Both mg zoo/hr ZFIL20 F10.0 none 0.001 none l/mg zoo/hr Both ZRES20 F10.0 .0015 1.0E-6 none /hr Both ZD F10.0 .0001 1.0E-6 none /hr Both OXZD F10.0 .03 1.0E-6 none /hr Both--------------------------------------------------------------------------------
Explanation
MZOEAT is the maximum zooplankton unit ingestion rate.ZFIL20 is the zooplankton filtering rate at 20 degrees C.ZRES20 is the zooplankton unit respiration rate at 20 degrees C.ZD is the natural zooplankton unit death rate.OXZD is the increment to unit zooplankton death rate due to anaerobic conditions.
RCHRES -- Section PLANK Input
606
4.4(3).8.3.9 Table-type ZOO-PARM2 -- Second group of zooplankton parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
ZOO-PARM2 <-range><--------------zoo-parm2---------------> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END ZOO-PARM2
******* Example *******
ZOO-PARM2 RCHRES TCZFIL TCZRES ZEXDEL ZOMASS*** # - # mg/org*** 1 7 1.2 1.1 0.8 END ZOO-PARM2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<zoo-parm2> TCZFIL F10.0 1.17 1.0 2.0 none Both TCZRES F10.0 1.07 1.0 2.0 none Both ZEXDEL F10.0 0.7 .001 1.0 none Both ZOMASS F10.0 .0003 1.0E-6 1.0 mg/org Both--------------------------------------------------------------------------------
Explanation TCZFIL and TCZRES are the temperature correction coefficients for filtering andrespiration, respectively.
ZEXDEL is the fraction of non-refractory zooplankton excretion which is immediatelydecomposed when the ingestion rate is greater than MZOEAT.
ZOMASS is the average weight of a zooplankton organism.
RCHRES -- Section PLANK Input
607
4.4(3).8.3.10 Table-type BENAL-PARM -- Benthic algae parameters
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
BENAL-PARM <-range><---------benal-parm---------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END BENAL-PARM
******* Example *******
BENAL-PARM RCHRES MBAL CFBALR CFBALG*** # - # mg/m2 *** 1 7 520. .56 .80 END BENAL-PARM
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<benal-parm> MBAL F10.0 600. .01 none mg/m2 Both CFBALR F10.0 1.0 .01 1.0 none Both CFBALG F10.0 1.0 .01 1.0 none Both--------------------------------------------------------------------------------
Explanation
MBAL is the maximum benthic algae density (as biomass).
CFBALR and CFBALG are the ratios of benthic algal to phytoplankton respiration andgrowth rates, respectively.
RCHRES -- Section PLANK Input
608
4.4(3).8.3.11 Table-type PLNK-INIT -- Initial plankton conditions
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PLNK-INIT <-range><----------------------plank-init--------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END PLNK-INIT
*******Example*******
PLNK-INIT RCHRES PHYTO ZOO BENAL ORN ORP ORC*** # - # mg/l org/l mg/m2 mg/l mg/l mg/l*** 1 7 .0001 .05 .002 .01 .02 .01 END PLNK-INIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<plank-init> PHYTO F10.0 .96E-6 1.0E-10 none mg/l Both ZOO F10.0 .03 1.0E-6 none org/l Both BENAL F10.0 1.0E-8 1.0E-10 none mg/m2 Both ORN F10.0 0.0 0.0 none mg/l Both ORP F10.0 0.0 0.0 none mg/l Both ORC F10.0 0.0 0.0 none mg/l Both--------------------------------------------------------------------------------
Explanation
PHYTO is the initial phytoplankton concentration, as biomass.ZOO is the initial zooplankton concentration.BENAL is the initial benthic algae density, as biomass.ORN is the initial dead refractory organic nitrogen concentration.ORP is the initial dead refractory organic phosphorus concentration.ORC is the initial dead refractory organic carbon concentration.
RCHRES -- Section PHCARB Input
609
4.4(3).8.4 RCHRES-BLOCK -- Section PHCARB input
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
[Table-type PH-PARM1] [Table-type PH-PARM2] [Table-type PH-INIT ]
********************************************************************************
Explanation
The exact format of each of the tables above is detailed in the documentation whichfollows. Tables in brackets [] need not always be supplied; for example, becauseall of the inputs have default values.
RCHRES -- Section PHCARB Input
610
4.4(3).8.4.1 Table-type PH-PARM1 -- Flags for pH simulation
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PH-PARM1 <-range><ph-parm1> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END PH-PARM1
******* Example *******
PH-PARM1 RCHRES PHCN ALKC*** # - # *** 1 7 30 9 END PH-PARM1
********************************************************************************
Details------------------------------------------------------------Symbol Fortran Format Def Min Max name(s)------------------------------------------------------------<ph-parm1> PHCNT I5 25 1 100 ALKCON I5 1 1 10------------------------------------------------------------
Explanation
PHCNT is the maximum number of iterations used to solve for the pH.
ALKCON is the number of the conservative substance (in section CONS) which is usedto simulate alkalinity. Alkalinity must be simulated in order to obtain validresults.
RCHRES -- Section PHCARB Input
611
4.4(3).8.4.2 Table-type PH-PARM2 -- Parameters for pH simulation
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PH-PARM2 <-range><----------ph-parm2----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PH-PARM2
******* Example *******
PH-PARM2 RCHRES CFCINV BRCO2(1) BRCO2(2)*** # - # mg/m2.hr mg/m2.hr*** 1 7 .901 72.0 65.1 END PH-PARM2
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system ------------------------------------------------------------------------------- <ph-parm2> CFCINV F10.0 .913 .001 1.0 none Both BRCO2(1) F10.0 62. .01 none mg/m2.hr Both BRCO2(2) F10.0 62. .01 none mg/m2.hr Both --------------------------------------------------------------------------------
Explanation
CFCINV is the ratio of the carbon dioxide invasion rate to the oxygen reaerationrate (which is computed in section OXRX).
BRCO2 is the benthal release rate of CO2 (as carbon) for: (1) aerobic and (2)anaerobic conditions.
RCHRES -- Section PHCARB Input
612
4.4(3).8.4.3 Table-type PH-INIT -- Initial conditions for pH simulation
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PH-INIT <-range><----------ph-init-----------> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PH-INIT
******* Example *******
PH-INIT RCHRES TIC CO2 PH*** # - # mg/l mg/l *** 1 7 2.0 .03 8.0 END PH-INIT
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ph-init> TIC F10.0 0.0 0.0 none mg/l Both CO2 F10.0 0.0 0.0 none mg/l Both PH F10.0 7.0 1.0 15.0 none Both--------------------------------------------------------------------------------
Explanation
TIC is the initial total inorganic carbon.CO2 is the initial carbon dioxide (as carbon).PH is the initial pH.
COPY Block
613
4.4(11) COPY Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** COPY Table-type TIMESERIES END COPY
********************************************************************************
Explanation
The COPY module is used to copy one or more time series from one location (source)to another (target). See Section 4.2(11) in Part E for a detailed description ofits function.
COPY Block
614
4.4(11).1 Table-type TIMESERIES -- Number of time series to be copied
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
TIMESERIES <-range><npt><nmn> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END TIMESERIES
******* Example *******
TIMESERIES Copy-opn *** # - # NPT NMN*** 1 7 4 END TIMESERIES
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<npt> NPT I5 0 0 20<nmn> NMN I5 0 0 20----------------------------------------------------------
Explanation
NPT is the number of point-valued time series to be copied. NMN is the number of mean-valued time series to be copied.
PLTGEN Block
615
4.4(12) PLTGEN Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******PLTGEN Table-type PLOTINFO Table-type GEN-LABELS Table-type SCALING Table-type CURV-DATA (repeats for each time series to be written to PLTGEN file)END PLTGEN********************************************************************************
Explanation
The PLTGEN module prepares one or more time series for display on a plotter. Itwrites the time series, and associated title and scaling information, to a "pltgen"file which must be input to a stand-alone program that translates the data intocommands that drive the plotter. See Section 4.2(12) of Part E for further details.
4.4(12).1 Table-type PLOTINFO -- General plot information
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PLOTINFO <-range><fil><npt><nmn><lab><pyr><piv> . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . END PLOTINFO******* Example *******
PLOTINFO Plot-opn *** # - # FILE NPT NMN LABL PYR PIVL *** 1 3 2 END PLOTINFO
********************************************************************************
PLTGEN Block
616
Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<fil> PLOTFL I5 30 30 99<npt> NPT I5 0 0 10<nmn> NMN I5 0 0 10<lab> LABLFG I5 0 -1 1 <pyr> PYREND I5 9 1 12<piv> PIVL I5 1 -2 1440----------------------------------------------------------
Explanation
PLOTFL is the file unit number of the PLTGEN file (output of this operation).
NPT is the number of point-valued time series to be written to the file.
NMN is the number of mean-valued time series to be written to the file.
LABLFG indicates how the plot will be labeled:
-1 means no labels
0 means standard labeling; that is, one set of X and Y axes and associated labels will be drawn for entire plot.
1 means separate X and Y axes and labels will be drawn for each "frame" of the plot (e.g., each water year).
PYREND is the calendar month which terminates a plot frame (eg. a water year).
PIVL is the number of basic time intervals (DELT minutes each) to be aggregated toget to the interval of the data written to the PLTGEN file. A PIVL of -1 causesa monthly PLTGEN file to be written. A PIVL of -2 causes an annual PLTGEN file tobe written.
PLTGEN Block
617
4.4(12).2 Table-type GEN-LABELS -- General plot labels
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GEN-LABELS <-range><--------------- title ----------------> <------ylabl-------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GEN-LABELS
******* Example *******
GEN-LABELS Plot-opn *** # - # General title Y-axis label *** 1 3 Reservoir inflow and outflow rates Flow (ft3/sec) END GEN-LABELS
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------
<title> TITLE 10A4 none none none<ylabl> YLABL 5A4 none none none
----------------------------------------------------------
Explanation
TITLE is the general plot title.
YLABL is the label to be placed on the Y-axis.
PLTGEN Block
618
4.4(12).3 Table-type SCALING -- Scaling information
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SCALING <-range><--ymin--><--ymax--><--ivlin-><-thresh-> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END SCALING
******* Example *******
SCALING Plot-opn *** # - # YMIN YMAX IVLIN THRESH *** 1 3 500. 48. END SCALING
********************************************************************************
Details--------------------------------------------------------------------------------Symbol Fortran Format Def Min Max Units Unit name(s) system--------------------------------------------------------------------------------<ymin> YMIN F10.0 0.0 none none See Note Both<ymax> YMAX F10.0 none none none See Note Both<ivlin> IVLIN F10.0 none 0.0 none ivl/in Both<thresh> THRESH F10.0 -1.0E30 none none See Note Both--------------------------------------------------------------------------------
Note: Units are defined by the user, in field YLABL of Table-type GEN-LABELS
Explanation
YMIN and YMAX are the minimum and maximum ordinate (Y axis) values.
IVLIN is the horizontal (time) scale; that is, number of intervals (in pltgen file)per inch on graph.
THRESH is the write threshold value. If the value for any time series is greaterthan the threshold, a full record is written to the PLTGEN file for the currentPLTGEN file time interval.
PLTGEN Block
619
4.4(12).4 Table-type CURV-DATA -- Data for each time series on pltgen file (Table must be repeated for each time series on the pltgen file)
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
CURV-DATA <-range> <-----label----><lin><int><col> <tr> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END CURV-DATA
******* Example *******
CURV-DATA Plot-opn Curve label Line Intg Col Tran *** # - # type eqv code code *** 1 3 Inflow 10 1 1 AVER END CURV-DATA
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<label> LABEL 4A4 none none none<lin> LINTYP I5 0 none none<int> INTEQ I5 0 0 13<col> COLCOD I5 0 0 10<tr> TRAN A4 SUM none none----------------------------------------------------------
PLTGEN Block
620
Explanation
LABEL is a descriptive label for this particular curve (time series).
LINTYP describes the type of line to be drawn for this curve. It also determinesthe frequency of plotted symbols:
A zero value means points are connected by straight lines; no symbols are drawn at individual data points.
A positive value means points are connected by straight lines; the magnitudedetermines the frequency of plotted symbols (e.g., 4 means plot a symbol atevery 4th point obtained from the pltgen file).
A negative value means no connecting lines are drawn. Only symbols are plotted; the absolute value determines the frequency (as above).
INTEQ is the integer equivalent of the symbols to be plotted for this curve (i.e.,indicates which symbol to use). It is only meaningful if LINTYP is not zero.Value of 2 might mean a triangle, etc.
COLCOD is the color code for this curve. The meaning depends on how the stand-alone plot program is set up; e.g., 1 might mean red pen, 2 blue pen, etc.
TRAN is the transformation code used to aggregate data from the basic interval(internal time step) to the PLTGEN file interval. Valid values are: SUM, AVER,MAX, MIN, and LAST.
Note: The stand-alone program, which reads the pltgen file and drives the plotter,must translate these data into plotter commands.
DISPLY Block
621
4.4(13) DISPLY Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** DISPLY Table-type DISPLY-INFO1 [Table-type DISPLY-INFO2]END DISPLY
********************************************************************************
Explanation
The DISPLY module summarizes a time series and presents the results in neatlyformatted tables. Data can be displayed at any HSPF-supported interval. SeeSection 4.2(13) of Part E for further information.
DISPLY Block
622
4.4(13).1 Table-type DISPLY-INFO1 -- Contains most of the information necessary to generate data displays.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
DISPLY-INFO1 <-range><----------title-----------> <tr><piv> d<fil><pyr> d<fil><ynd> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END DISPLY-INFO1
******* Example *******
DISPLY-INFO1 #through#***<----------Title--------> <-short-span-> *** <---disply---> <annual summary -> *** TRAN PIVL DIG1 FIL1 PYR DIG2 FIL2 YRND 1 Daily precip in TSS #20 (in) 1 2 20 6 2 Simulated soil temp (Deg C) AVER 4 1 21 1 1 22 6 END DISPLY-INFO1
********************************************************************************Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<title> TITLE(*) 7A4 none none none<tr> TRAN A4 SUM none none<piv> PIVL I5 0 0 1440d DIGIT1 A1 0 0 7 <fil> FILE1 I5 6 6 99<pyr> PYRFG I5 0 0 1 d DIGIT2 A1 0 0 7 <fil> FILE2 I5 6 6 99<ynd> PYREND I5 9 1 12----------------------------------------------------------
DISPLY Block
623
Explanation
TITLE is the title that will be printed at the top of each page of the display.
TRAN is the transformation code, used to aggregate data from the basic interval(internal time step) to the various display intervals (for both short- andlong-span displays). Valid values are: SUM, AVER, MAX, MIN, LAST.
PIVL is the number of basic time intervals (DELT mins each) to be aggregated to getto the interval of the data printed in a short-span display (e.g., In the aboveexample, if DELT were 15 mins for DISPLY operation #2, then the data in theshort-span summary tables would be displayed at an interval of 1 hour (PIVL=4). IfPIVL=0, a short-span display is not produced.
DIGIT1 and DIGIT2 are the number of decimal digits to be used to print data in theshort-span and long-span displays, respectively. Note that it is up to the userto ensure that this value falls in the valid range 0-7. HSPF does not check this.
FILE1 and FILE2 are the file unit numbers of the files to which short-and long-span displays will be routed.
PYRFG indicates whether or not a long-span display (annual summary of daily values)is required. Value of 1 means it is, 0 means it is not.
PYREND is the calendar month which will appear at the right-hand extremity of anannual summary. This enables the user to decide whether the data should bedisplayed on a calendar year or some other (e.g., water year) basis.
DISPLY Block
624
4.4(13).2 Table-type DISPLY-INFO2 -- Additional optional information for module DISPLY.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** DISPLY-INFO2 <-range><--mult--><---add--><-thresh1><-thresh2> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END DISPLY-INFO2
******* Example *******
DISPLY-INFO2 #through# Convert DegC to F Display negative data *** Mult Add THRSH1 *** 2 5 1.8 32.0 -999. END DISPLY-INFO2
********************************************************************************
Details------------------------------------------------------------------------------- Symbol Fortran Format Def Min Max Units Unit name(s) system------------------------------------------------------------------------------- <mult> A F10.0 1.0 none none none Both<add> B F10.0 0.0 none none none Both<thresh1> THRESH1 F10.0 0.0 none none none Both<thresh2> THRESH2 F10.0 0.0 none none none Both-------------------------------------------------------------------------------
DISPLY Block
625
Explanation
This table is usually not supplied.
A and B are parameters used to convert the data from internal units to displayunits: Display value= A*(internal value)+B
The conversion is done before any aggregation of data to larger time steps (i.e.,larger than the simulation time interval) is performed. Note that the defaultvalues of A and B result in no change.
THRSH1 and THRSH2 are threshold values for the short-span and long-span displays,respectively (THRSH2 is not presently used). THRSH1 can be used to reduce thequantity of printout produced in a short-span display; it functions as follows:When the individual values in a row of the display have been aggregated to get the"row value" (hour- or day-value, depending on the display interval), if therow-value is greater than THRSH1 the row is printed, else it is omitted. Thus, forexample, the default of 0.0 will ensure that rows of data containing all zeros areomitted.
DURANL Block
626
4.4(14) DURANL Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
DURANL Table-type GEN-DURDATA [Table-type SEASON] [Table-type DURATIONS] [Table-type LEVELS] [Table-type LCONC] END DURANL
********************************************************************************
Explanation
The DURANL module performs duration and excursion analysis on a time series. Forexample, it analyzes the frequency with which N consecutive values in the timeseries exceed a specified set of values, called "levels". N is the "duration" ofthe excursion; up to 10 durations may be used in one duration analysis operation.The user may specify that only those data falling within a specified time in eachyear (analysis season) be processed. For further details see Section 4.2(14) ofPart E.
DURANL Block
627
4.4(14).1 Table-type GEN-DURDATA -- General information for duration analysis
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
GEN-DURDATA <-range><----------------title-----------------><-nd><-nl><-pr><-pu> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END GEN-DURDATA
******* Example *******
GEN-DURDATA #through#<***--------------title----------------> NDUR NLEV PRFG P- LCNU LCOU *** UNIT 1 Simulated DO in Reach 40 5 2 2 0 END GEN-DURDATA
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<title> TITLE(*) 10A4 none none none<nd> NDUR I5 1 1 10<nl> NLEV I5 1 1 20<pr> PRFG I5 1 1 7 <pu> PUNIT I5 6 1 99<lcn> LCNUM I5 0 0 5 <lco> LCOUT I5 0 0 1 ----------------------------------------------------------
DURANL Block
628
Explanation
TITLE is the title which the user gives to the duration analysis operation;usually, something which identifies the time series being analyzed.
NDUR is the number of durations for which the time series will be analyzed.
NLEV is the number of user-specified levels which will be used in analyzing thetime series.
PRFG is a flag which governs the quantity of information printed out. A value of1 results in minimal (basic) output. Increasing the value (up to the maximum of7) results in increased detail of output.
PUNIT is the file unit number to which the output of the duration analysisoperation will be written. Each duration analysis operation must have a uniquefile unit number.
LCNUM indicates the number of lethal concentration curves to be used in theanalysis. A zero means no lethality analysis is to be performed.
LCOUT is a flag which governs the printout of intermediate lethal event information(1=on).
4.4(14).2 Table-type SEASON -- The analysis season for the durational analysis
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SEASON <-range> <---start--> <----end---> . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . END SEASON******* Example *******
SEASON *** Start End #through#*** mo da hr mn mo da hr mn 1 10 02 02 END SEASON
********************************************************************************
DURANL Block
629
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<start> SESONS(2-5) 4(1X,I2) see below<end> SESONE(2-5) 4(1X,I2) see below----------------------------------------------------------
Explanation
This table is used if one wishes to specify an analysis season; that is, only datafalling between the specified starting and ending month/day/hour/minute (in eachyear) should be considered.
Note:
1. The defaults, minima, maxima and other values for specifying the starting andending date/times are the same as those given in the discussion of the GLOBALBlock (Section 4.2). If any fields in the starting date/time are blank theydefault to the earliest meaningful value; for the ending date/time they defaultto the latest possible values. Thus, the analysis season in the example aboveincludes the entire month of February.
2. Although it is not meaningful to provide for a "year" in the fields documented above (since the analysis season applies to every year in the run), the four spaces preceding both the <start> and <end> fields should beleft blank because the system does, in fact, read the year and expects it tobe blank or zero.
3. The defaults imply that, if this table is omitted, the analysis season extends from January 1 through December 31.
DURANL Block
630
4.4(14).3 Table-type DURATIONS -- Durations to be used in the analysis
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
DURATIONS <-range><-d1><----------------others---------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END DURATIONS
******* Example *******
DURATIONS #through#***<---Durations---------------------------------> *** 1 2 3 4 5 1 2 1 10 15 20 40 3 1 20 21 22 END DURATIONS
********************************************************************************
Details----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<d1> DURAT(1) I5 1 1 1 <others> DURAT(2-10) 9I5 2 2 none----------------------------------------------------------
Explanation
DURAT(*) is an array which contains the NDUR different durations for which the timeseries will be analyzed (NDUR was specified in Table-type GEN-DURDATA). Thedurations are expressed in multiples of the internal time step specified in the OPNSEQUENCE Block (Section 4.3). Thus, if DELT= 5 min and the duration is 3, the timeseries will be analyzed with a window of 15 minutes. The analysis algorithmrequires that the first duration be 1 time step, but the others can have anyinteger value.
DURANL Block
631
4.4(14).4 Table-type LEVELS -- Levels to be used in the analysis
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
LEVELS <-range><-------------------------- first 14 --------------------------------> <-range><-------------------------- last 6 ------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END LEVELS
******* Example *******
LEVELS #through#*** 2 3 4 5 6 7 8 9 10 11 12 13 14 15 #through#***16 17 18 19 20 21 1 -30. -10. 0. 10. 20. 40. 80. 100. 200.1000. 2.E3 3.E3 5.E3 1.E4 1 2.E4 3.E4 #through#*** 2 3 4 5 2 -20. 0. 20. 50. END LEVELS
********************************************************************************
Details----------------------------------------------------------- Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------- <first14> LEVEL(2-15) 14F5.0 0.0 none none<last6> LEVEL(16-21) 6F5.0 0.0 none none-----------------------------------------------------------
DURANL Block
632
Explanation
LEVEL(2 through 21) contains the 20 possible user-specified levels for which theinput time series will be analyzed. (LEVEL(1) and LEVEL(22) are reserved forsystem use, and this does not affect the user since only LEVEL(2) through (21) canbe specified). The actual number of levels (NLEV) was specified in Table-typeGEN-DURDATA. If NLEV is greater than 14, the entry for a given operation must becontinued to the next line; up to 2 lines may be required to cover all the levels.In the example above, operation 1 has 16 user-specified levels and thus requires2 lines, but operation 2 only requires 1 line because it has only 4 user-specifiedlevels. When an entry has to be continued onto more than 1 line: 1. No blank or comment lines may be put between any of the lines for a continued
entry. Put all comments ahead of the entry. (See operation 1 in above example).
2. The <range> specification must be repeated for each line onto which the entryis continued.
Note that the levels must be specified in ascending order. The system checks thatthis requirement is not violated.
DURANL Block
633
4.4(14).5 Table-type LCONC -- Lethal concentrations to be used in the analysis Table repeats for each lethal concentration curve-LCNUM times
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** LCONC <-range><------------------------------first-7-------------------------------> <-range><-----------last-3-----------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END LCONC ******* Example ******* LCONC # - #*** LC1 LC2 LC3 LC4 LC5 LC6 LC7 # - #*** LC8 LC9 LC10 1 2 1. 3. 6. 8. 15. 5. 8. 1 2 20. 30. 60. END LCONC ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<first-7> LCONC(1-7,I) 7F10.0 0.0 none none<last-3> LCONC(8-10,I) 3F10.0 0.0 none none---------------------------------------------------------- Explanation LCONC(*) is an array which contains the NDUR different lethal levels which are usedin a lethal concentration analysis. If no lethality analysis is being done, thistable may be omitted.
GENER Block
634
4.4(15) GENER Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** GENER Table-type OPCODE [Table-type NTERMS] only required if OPCODE = 8 [Table-type COEFFS] only required if OPCODE = 8, [Table-type PARM] only required if OPCODE = 9, 10, 11, 24, 25, or 26 END GENER ******************************************************************************** Explanation The GENER module generates a time series from one or two input time series.Usually, only Table-type OPCODE is required. However, if OPCODE = 8 (power series),you need to supply the number of terms in the power series and the values of thecoefficients. If OPCODE = 9, 10, 11, 24, 25, or 26, then Table-type PARM isrequired to input the constant required in the operation.
GENER Block
635
4.4(15).1 Table-type OPCODE -- Operation code for time series generation ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** OPCODE <-range><opn> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END OPCODE ******* Example ******* OPCODE #through# OP- *** CODE *** 1 3 8 5 20 END OPCODE ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<opn> OPCODE I5 none 1 26----------------------------------------------------------
GENER Block
636
Explanation OPCODE is the operation code. If A and B are the input time series and C is thegenerated time series, the functions performed for the allowable range of valuesof OPCODE are: OPCODE Definition 1 C= Abs(A) 2 C= Sqrt(A) 3 C= Trunc(A) 4 C= Ceil(A) 5 C= Floor(A) 6 C= loge(A) 7 C= log10(A) 8 C= K(1)+K(2)*A+K(3)*A**2+(up to 7 terms) 9 C= K**A 10 C= A**K 11 C= A+K 12 C= Sin(A) 13 C= Cos(A) 14 C= Tan(A) 15 C= Sum(A) 16 C= A+B 17 C= A-B 18 C= A*B 19 C= A/B 20 C= Max (A,B) 21 C= Min (A,B) 22 C= A**B 23 C= cumulative departure of A below B 24 C= K 25 C= Max (A,K) 26 C= Min (A,K) If OPCODE is less than 15, or OPCODE equals 25 or 26, only one input time seriesis required; if OPCODE is 24, no input time series are required; otherwise twoinput time series are required. Note that the operation is performed on the datawhen they are in internal form (timestep=DELT, units=internal units). For furtherdetails, see Section 4.2(15) of Part E.
GENER Block
637
4.4(15).2 Table-type NTERMS -- Number of terms in power series ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** NTERMS <-range><-nt> . . . . . . . (repeats until all operations of this type are covered) . . . . . . . END NTERMS ******* Example ******* NTERMS #through#NTERMS *** 1 2 4 END NTERMS ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<nt> NTERMS I5 2 1 7 ---------------------------------------------------------- Explanation This table is only relevant if OPCODE=8. NTERMS is the total number of terms in thepower series: C= K(1)+K(2)*A+K(3)*A**2 etc. The default value of 2 was chosen because this option will probably be used mostoften (to perform a linear transformation).
GENER Block
638
4.4(15).3 Table-type COEFFS -- Coefficients in generating power function ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** COEFFS <-range><----------------------------coeffs----------------------------------> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . END COEFFS ******* Example ******* COEFFS #through# *** K1 K2 K3 1 7 -2.0 1.5 0.2 END COEFFS ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<coeffs> K(*) 7F10.0 0.0 none none---------------------------------------------------------- Explanation This table is only relevant if OPCODE=8. K(1 through NTERMS) are the coefficientsin the power function: C= K(1)+K(2)*A+K(3)*A**2+ etc.
GENER Block
639
4.4(15).4 Table-type PARM -- Constant for GENER operation
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** PARM <-range><--con---> . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . END PARM ******* Example ******* PARM # - # *** K 1 7 2.5 END PARM ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<con> K F10.0 1.0 none none---------------------------------------------------------- Explanation This table is only relevant if OPCODE is 9, 10, 11, 24, 25, or 26. K is the constant required in the operation.
MUTSIN Block
640
4.4(16) MUTSIN Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MUTSIN Table-type MUTSINFO END MUTSIN ******************************************************************************** Explanation The MUTSIN module is used to copy one or more time series from a PLTGEN file or itsequivalent to one or more targets. The targets may be data sets in the TSS or WDM(specified in the EXT-TARGETS Block) or input time series in other operations(specified in the NETWORK Block). See Section 4.2(16) in Part E for a detaileddescription of MUTSIN's function.
MUTSIN Block
641
4.4(16).1 Table-type MUTSINFO -- Information about time series to be copied
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MUTSINFO <-range><mfl><npt><nmn><nli><mis> . . . . . . . . . . . . . . . . . (repeats until all operations of this type are covered) . . . . . . . . . . . . . . . . . END MUTSINFO ******* Example ******* MUTSINFO # - # MFL NPT NMN NLI MSFG *** 1 30 1 1 25 0 END MUTSINFO ******************************************************************************** Details ----------------------------------------------------------Symbol Fortran Format Def Min Max name(s)----------------------------------------------------------<mfl> MUTFL I5 30 30 99<npt> NPT I5 0 0 10<nmn> NMN I5 0 0 10<nli> NLINES I5 25 1 none<mis> MISSFG I5 0 0 3 ---------------------------------------------------------- Explanation MUTFL is the file unit number of the PLTGEN-format file being input. NPT is the number of point-valued time series to be input.NMN is the number of mean-valued time series to be input. NLINES is the number of lines to skip at the beginning of the file.MISSFG is the missing data action flag. 0 - stop on missing data 1 - fill missing data with 0.0 2 - fill missing data with -1.0E30 3 - fill missing data with next value
FTABLES Block
642
4.5 FTABLES Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout ******
FTABLES
FTABLE <t> <----ftab-parms----> <-------------- row-of-values -------------------------------------------------> ........................................................................... line above repeats until function has been described through desired range ........................................................................... END FTABLE<t> Any number of FTABLES may appear in the block END FTABLES
********************************************************************************
Details -------------------------------------------------------------------------------- Symbol FORTRAN Format Comment Name(s) --------------------------------------------------------------------------------<t> NUMBR I3 User's ID number for this FTABLE.
<ftab-parms> Fparms(4) 4I5 Up to 4 control parameters may be supplied for an Ftable, e.g. number of rows, number of columns, etc. Exact details depend on the FTABLE concerned.
<row-of-values> VAL(*) variable Each column is dedicated to one of the variables in the function. Each row contains a full set of corresponding values of these variables, e.g., depth, surface area, volume, and outflow for a RCHRES.-------------------------------------------------------------------------------
FTABLES Block
643
Explanation An FTABLE is used to specify, in discrete form, a functional relationship betweentwo or more variables. For example, in the RCHRES module, it is assumed that thereis a fixed relationship between depth, surface area, volume, and volume-dependent(F(vol)) discharge component. An FTABLE is used to document this non-analyticfunction in numerical form. Each column of the FTABLE is dedicated to one of theabove variables, and each row contains corresponding values of the set. That is,each row contains the surface area, volume, and discharge for a given depth. Thenumber of rows in the FTABLE will depend on the range of depth to be covered andthe desired resolution of the function. 4.5(3) FTABLES for the RCHRES Application Module 4.5(3).1 FTABLE for HYDR section The geometric and hydraulic properties of a RCHRES are summarized in a functiontable (FTABLE). Every RCHRES must be associated with one FTABLE; the associationis done in Table-type HYDR-PARM2 (Section 4.4(3).2.2 above). Usually, every RCHRESwill have its own FTABLE; however, if RCHRES's are identical they can share thesame FTABLE. FTABLE's may be included in the user's input (FTABLES Block) or theymay be stored in a WDM File.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** FTABLE <t> <-nr><-nc><-depth--><--area--><-volume-><-------------- f(VOL)-values -------------------> ........................................................................ The above row repeats until values have been supplied to cover the entire cross-section at the desired resolution ........................................................................ END FTABLE<t> Example ******* FTABLE 103 rows cols *** 3 5 depth area volume outflow1 outflow2 *** (ft) (acres) (acre-ft) ( ft3/s) ( ft3/s) *** 0.0 0.0 0.0 0.0 0.0 5.0 10.0 25.0 20.5 10.2 20.0 120.0 1000.0 995.0 200.1 END FTABLE103********************************************************************************
FTABLES Block
644
Details --------------------------------------------------------------------------------Symbol Name(s) Format Comment--------------------------------------------------------------------------------<t> see Sect. 4.5 I3 ID No. of FTABLE
<nr> NROWS I5 Number of rows in FTABLE
<nc> NCOLS I5 Number of columns in FTABLE
<depth> Depth F10.0 Depth of RCHRES; Units: English = ft; Metric = m
<area> Surface area F10.0 Surface area of RCHRES; Units: English = acres; Metric = ha
<volume> Volume F10.0 Volume of RCHRES; The volume in the first row must be 0.0; Units: English= acre.ft; Metric= Mm3 (10**6 m3)
<f(vol)- F(V) (NCOLS-3)* Units: English = ft3/s; Metric = m3/s values> F10.0 --------------------------------------------------------------------------------
Explanation
This FTABLE lists depth, surface area and, optionally, one or more other values(typically discharge rates) as functions of volume. HSPF interpolates between thespecified values to obtain the geometric and hydraulic characteristics forintermediate values of volume. The FTABLE must satisfy the following conditions:
1. (NCOLS*NROWS) must not exceed 100 2. NCOLS must be between 3 and 8 3. There must be at least one row in the FTABLE4. The first row must have volume = 0.05. No negative values are permitted6. The depth and volume fields may not decrease as the row number increases
In the example given above, we have a reach with two outflows, both of which arefunctions of volume. Thus, there are 5 columns in the FTABLE.
The values for this type of FTABLE can either be supplied directly by the user orgenerated by a subsidiary program from more basic information (e.g., by backwateranalysis or Manning's equation for assumed uniform flow).
WDM FTABLES are stored in WDM "table" data sets, and accessed directly by HSPF.These data sets may be created and modified through the use of the ANNIE program.WDM FTABLES follow the same structure, and must satisfy the same conditions asFTABLES contained in the UCI.
Time Series Linkages
645
4.6 TIME SERIES LINKAGES 4.6.1 General Discussion In the EXTERNAL SOURCES, NETWORK, EXTERNAL TARGETS, and SCHEMATIC/MASS-LINK blocks,the user specifies those time series which are to be passed between pairs ofoperations in the same INGRP or between individual operations and external sources/targets (WDM Data sets, DSS Data records, or sequential files). The blocks arearranged in the form of tables, each containing one or more entries (rows). Eachentry contains source information, a multiplication factor, a transformationfunction, and target information. The entries in these blocks may be in any order. When a time series associated with a data set in a WDM file is referred to, theuser supplies the data-set number and the data-set name. This information mustagree with data supplied when the data set was created. WDM data sets andassociated attributes are created using the interactive program ANNIE. The usershould refer to the ANNIE User's manual for additional information.
Time series may also be associated with DSS data records in up to five differentDSS files. Each record, or group of records, is identified by a pathname, whichis specified in the PATHNAMES block, where it is associated with a data-set numberin the context of the current UCI file. No data-set name is specified.
If a DSS record is accessed as an external target, it is not necessary that therecord, or even the file, exist before the run. DSS records used as externalsources, however, must be already present in the specified DSS file. The user specifies time series which are input to, or output from, an operatingmodule by supplying a group name (<sgrp>, <tgrp>) and a member name plus one or twosubscripts (<smem><m#>, <tmem><m#>). The member information must be compatiblewith data given in the Time Series Catalog for the applicable operating module andgroup (Section 4.7). The user may route the same source to several targets by making several separateentries in a block, each referring to the same source, or by making use of the"range" feature provided in the <tvol>< range> field. This latter feature does notapply to entries in the EXT TARGETS Block. In either case the implication is thatdata from the source will be used repetitively, and each time will be multipliedby the specified factor and added to whatever else has already been routed to thespecified target. Conversely, several sources may be routed to a single target,except in the EXT TARGETS Block. This happens when several entries specifydifferent sources but the same target. Here, the implication is that the dataobtained from the several sources must be accumulated (added) before being used bythe target.
Time Series Linkages
646
4.6.1.1 WDM File Concepts
The WDM file is a binary, direct-access file that is organized into discrete datasets. Each data set consists of data as well as "attributes" that describe thedata. Disk space for a WDM file is allocated as needed in 40,960-byte increments.Space from deleted data sets within a WDM file is reused as new data are added tothe file. Thus the WDM file needs no special maintenance processing.
HSPF accesses WDM files for both input and output time series data. HSPF requiresthat a data set be created in an existing WDM file prior to any run that writes tothe data set. Maintenance of WDM files and creation of new data sets isaccomplished using the USGS's ANNIE program (Flynn, K.M., P.R. Hummel, A.M. Lumb,and J.L. Kittle, Jr. 1995. User*s Manual for ANNIE, Version 2, a Computer Programfor Interactive Hydrologic Data Management. WRI Report 95-4085. U.S. GeologicalSurvey, Reston, VA).
Within the HSPF UCI file, a WDM data set is referred to by its data-set number andits name (i.e., its TSTYPE attribute), which is a four-character alpha-numericidentifier. As stated above, WDM data-set attributes are created when the data setis first created using the ANNIE program. The attributes that are associated withtime series data sets can be divided into two types: 1) those that describe how thedata are stored in the data set, and 2) those that are purely descriptive orprovide information about the data. Examples of the second type are station name(STANAM), station ID (STAID), latitude and longitude (LATDEG, LNGDEG), and data-setdescription (DESCRP). Attributes of the first type are more critical, and areconsidered "required" attributes for time series data sets. These attributes aredefined below:
TCODE Time units code for defining the time interval of the data set (1-seconds,2-minutes, 3-hours, 4-days, 5-months, 6-years); valid values in HSPF are2, 3, 4, and 5.
TSSTEP Time interval of data set in TCODE units (used in combination with TCODE)
TSFORM Form of data; valid values in HSPF context are: (1-mean over time step, 2-total over time step, 3-instantaneous); 1 and 2 correspond to "mean" timeseries, and 3 corresponds to "point" time series.
TSBYR Starting year of data set; defaults to 1900; generally should be set to ayear just prior to start of data.
TGROUP Unit for group pointers (3-hours, 4-days, 5-months, 6-years, 7-centuries);it may affect speed of data retrievals and total amount of data storageavailable in data set; see table in ANNIE manual for recommended values.
TSFILL Filler value for missing data; default = 0.0.
COMPFG Compression flag (1-data are compressed, 2-data are not compressed)
TOLR Compression tolerance; data values within TOLR are compressed.
VBTIME Variable time step flag; must be 1 (all data at same time step) for HSPF.
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4.6.1.2 DSS File Concepts
DSS Pathnames
DSS files access time series data in a somewhat different manner than WDM files.The latter refers to a time series by a single data-set number. DSS files referto time series by "pathnames", which follow different conventions for differentkinds of data. HSPF uses only one of the allowed kinds, i.e., "Regular" TimeSeries. The PATHNAMES block is used to temporarily assign or associate a data-setnumber with each time series needed in the run. (See Section 4.6.6.)
An entire DSS time series is not necessarily stored in one logical piece in the DSSfile. Data are broken up into separate records with definite sizes and startingdates, which depend on the time step of the data. For instance, hourly data isstored in records each containing one month of values and starting with the firsthour of the month. Daily data, on the other hand, is stored yearly, in recordsstarting on January 1st.
The pathname can consist of up to 80 characters; because of limitations on UCI linelength, HSPF only allows 64 characters in DSS pathnames. Pathnames are separatedinto six parts (delimited by slashes "/"), which are referenced by the characters"A" through "F". For a "regular" time series, the conventions for the contents ofthe six parts are:
A River basin or project name B Location or gage identifier C Data variable, e.g. FLOW, PRECIP D Starting date for block of data in the format 01JAN1980. This part is not used by HSPF, and should be left empty. E Time interval - valid values are: 5MIN, 10MIN, 15MIN, 30MIN, 1HOUR, 2HOUR, 3HOUR, 4HOUR, 6HOUR, 12HOUR, 1DAY, 1WEEK, 1MON, 1YEAR F Additional user-defined descriptive information, e.g. OBSERVED, PLAN A
Any single part may contain up to 32 characters, but the total including slashesmust remain less than 80 for general DSS use, and less than 64 characters for HSPF(leaving the D part empty). A typical HSPF pathname might be:
/PATUXENT/BRIGHTON DAM/DIVERSION//1DAY/OBSERVED-CFS/
Note the double slash indicating the empty D part. A D part may be provided by theuser, but HSPF ignores it; this allows the DSS system to generate it, as needed,based on the starting and ending dates of the run. For additional information,users should refer to the HECDSS Users Guide (US Army Corps of Engineers HydrologicEngineering Center, April 1990).
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DSS Data Types
Each DSS data record also may have a data type string and/or a units string storedwith it. Units strings are ignored by HSPF. Data type strings are used todetermine whether the time series is point-valued or mean-valued in the context ofHSPF. Valid values of the data type string are:
INST-VAL - point-valued: instantaneous at end of interval PER-AVER - mean-valued: average over interval PER-CUM - mean-valued: total over interval
A fourth type, INST-CUM, which is used for mass curves, is not valid for HSPF. Thedata type string for each time series (input or output) must be specified in thePATHNAMES block.
The data type should not change over time (i.e. between subsequent records) for agiven time series. If a data record already exists before the run, any valuespecified in the PATHNAMES block must match the stored value, if it exists. If adata record is created by the run, it is stored with the value given in thePATHNAMES block, if any. If neither the record itself nor the PATHNAMES blockspecifies a data type, the data is treated by the program as a mean-valued timeseries.
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4.6.2 EXT SOURCES Block (External sources)
In this block the user specifies those time series which are to be supplied tooperations in a RUN from sources external to it; external sources are WDM datasets, DSS data sets, and sequential (SEQ) files.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
EXT SOURCES <svol><s#> <exsm>qf <ss><sg><-mfact--><tr> <tvol>< range> <tgrp> <tmem><m#> or <sfmt>f# .............................................................................Above line repeats until all external sources have been specified .............................................................................END EXT SOURCES
*******Example*******
EXT SOURCES<-Volume-> <Member> SsysSgap<--Mult-->Tran <-Target vols> <-Grp> <-Member-> ***<Name> # <Name> # tem strg<-factor->strg <Name> # # <Name> # # ***SEQ 3 HYDDAY ENGL 1.0 RCHRES 1 EXTNL ICONWDM1 22 PREC METRZERO SUM IMPLND 2 EXTNL PRECDSS 132 ENGL SAME PLTGEN 10 INPUT POINT 1END EXT SOURCES ********************************************************************************
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Details --------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column -------------------------------------------------------------------------------- <svol> SVOL 1 A6 External source volume. Valid values are
WDMn (Watershed Data Management System File,where n is 1-4 or blank), DSS (Data Storage System), and SEQ (sequential file).
<s#> SVOLNO 7 I4 Data-set number if SVOL = WDMn, or DSS; file unit number if SVOL = SEQ
<exsm> SMEMN 12 A6 Data-set TSTYP attribute if SVOL = WDMn; 12 A4 blank for SVOL = DSS
<sfmt> SFCLAS 12 A6 SFCLAS is a string indicating the class of format used in the sequential file.
qf QLFG 18 I2 Quality-of-data flag if SVOL = WDMn; specifies the minimum quality of WDM data which will be accepted by HSPF; valid values = 0-31; default = 31
f# SFNO 18 I2 SFNO identifies an object-time format supplied in the FORMATS Block. Default: standard format.
<ss> SSYST 21 A4 Unit system of data in the source if SVOL = SEQ, WDMn, or DSS; valid values: ENGL and METR; default = ENGL
<sg> SGAPST 25 A4 String indicating how missing lines in the sequential file, missing data in a DSS file, or WDM data of insufficient quality will be regarded; used if SVOL = SEQ, WDMn, or DSS. Valid values are ZERO (assign value 0) and UNDF (assign undefined value). Defaults to UNDF. See below for explanation.
<mfact> MFACTR 29 F10.0 The factor by which data from the source will be multiplied before being added to the target. Default = 1.0
<tr> TRAN 39 A4 String indicating which transformation function to use in transferring time series from source to target. See Section 4.6.7 for valid values and defaults.
<tvol> TVOL 44 A6 TVOL is the Operation-type of the target.
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< range> TOPFST 51 I3 TOPFST & TOPLST specify the range of operations TOPLST 55 I3 which are targets (e.g., PERLND 1 5). If TOPLST field is blank, the target is a single operation.
<tgrp> TGRPN 59 A6 Group to which the target time series belong(s).
<tmem> TMEMN 66 A6 Target time series member name.
<m#> TMEMSB(2) 72 2A2 Target time series member name subscripts; may be 2-character CATEGORY tag if applicable; must be integer otherwise. See Time Series Catalog.--------------------------------------------------------------------------------
Explanation If an entry specifies the source volume as SEQ, it is referring to a time seriescoming from a sequential file. The entry must therefore supply the file unitnumber and format information for the file. If an entry specifies the source volume as WDMn or DSS, the user is referring toa time series contained in the corresponding direct access data file: a WatershedData Management System file, or an HEC Data Storage System file. If the "n"portion of a WDM file reference is left blank, the program (by default) looks inthe first WDM file only.?
Note: TSS functionality is not included in the documentation.
When data are read from a WDM data set, the user may optionally supply a dataquality flag (QLFG), which will be compared with the data quality "tag" associatedwith all WDM time series data. Any data having lower quality than specified (valuegreater than QLFG) will be rejected and assigned the value specified by the WDMattribute TSFILL (if defined for the data set), or alternatively, if TSFILL is notavailable, by SGAPST.
When data are read from a sequential file the user supplies: 1. A "format class code". It fixes the nature and sequence of data in a typical
record (e.g. day and hour, followed by 12 hourly values).
2. The number of an object-time format, situated in the FORMATS Block. It fixesthe exact format of the data in a record. A default format can be selected bysupplying the number 0, or leaving the field blank.
The format classes and associated default formats presently supported in the HSPFsystem are documented in Section 4.9.
Note: All character strings must be left-justified in their fields except WDM dataset names (<exsm>) which must be justified in the same way that they were when thedata-set label or WDM attribute TSTYP was created.
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4.6.3 NETWORK Block
In this block the user specifies those time series which will be passed betweenoperations via the internal scratch pad (INPAD). If there are no such linkages theblock can be omitted. For many applications, particularly large or complexwatersheds that have many entries in the NETWORK block, the alternative use of theSCHEMATIC/MASS-LINK blocks may provide a simpler and more conceptual format forspecifying the linkages in the NETWORK block.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** NETWORK<svol><o#> <sgrp> <smem><m#><-mfact--><tr> <tvol>< range> <tgrp> <tmem><m#> .............................................................................Above line repeats until all network entries have been made .............................................................................END NETWORK *******Example******* NETWORK<-Volume-> <-Grp> <-Member-><--Mult-->Tran <-Target vols> <-Grp> <-Member-> ***<Name> # <Name> # #<-factor->strg <Name> # # <Name> # # ***RCHRES 1 HYDR ROVOL 0.5 RCHRES 2 EXTNL IVOLRCHRES 2 HYDR ROVOL RCHRES 5 EXTNL IVOLRCHRES 4 HYDR ROVOL RCHRES 5 EXTNL IVOLEND NETWORK
********************************************************************************
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Details
--------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column --------------------------------------------------------------------------------<svol> SVOL 1 A6 SVOL is the Operation-type of the source operation
<o#> SVOLNO 7 I4 Source Operation-type number (e.g., PERLND 5)
<sgrp> SGRPN 12 A6 Group to which the source time series belong(s).
<smem> SMEMN 19 A6 Source time series member name; see Time Series Catalog.
<m#> SMEMSB(2) 25 2A2 Source member name subscripts; may be 2-character CATEGORY tag if applicable; must be integer otherwise. See Time Series Catalog.
<mfact> MFACTR 29 F10.0 The factor by which data from the source will be multiplied before being added to the target. Default (blank field)= 1.0
<tr> TRAN 39 A4 String indicating which transformation function to use in transferring time series from source to target. See Section 4.6.7 for defaults, etc.
<tvol> TVOL 44 A6 Operation-type of the target.
< range> TOPFST, 51 I3 TOPFST & TOPLST specify the range of operations TOPLST 55 I3 which are targets (e.g. PERLND 1 5). If TOPLST field is blank, the target is a single operation.
<tgrp> TGRPN 59 A6 Group to which the target time series belong(s).
<tmem> TMEMN 66 A6 Target member name; see Time Series Catalog.
<m#> TMEMSB(2) 72 2A2 Target member name subscripts; may be 2-character CATEGORY tag if applicable; must be integer otherwise. See Time Series Catalog.--------------------------------------------------------------------------------
Explanation
The example above shows how this block is used to specify the connectivity of a setof reaches of stream channel (RCHRES 1 flows to RCHRES 2, RCHRES 2 and 4 flow toRCHRES 5). It can also be used to specify the flow of time series data fromutility operations to simulation operations and vice versa. The network can beextremely complex, or non-existent (e.g., if the RUN involves only one operation). Because the time series are transferred via the INPAD, each source and target pairmust be in the same INGRP.
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4.6.4 SCHEMATIC and MASS-LINK Blocks
The SCHEMATIC and MASS-LINK blocks work in tandem to allow the user to specify thewatershed structure and linkages in a more efficient and conceptual manner than ispossible using the NETWORK block.
The SCHEMATIC block contains global specifications of the watershed structure,i.e., connections of land segments to stream reaches and reach-reach connections.This block permits the user to input the area of a land segment that is tributaryto a stream reach in a single entry, instead of including the area in multipleentries in the NETWORK block. Each entry in the SCHEMATIC block refers to a tablein the MASS-LINK block where the detailed time series connections for that entryare specified.
The MASS-LINK block contains the specific time series to be transferred from oneoperation to another. This block also contains any required units conversionfactors or other multiplication factors that may be needed in addition to the area.For example, when runoff from a land segment is transferred to a stream reach, aconversion factor of 1/12 (0.08333) is needed to convert the runoff from inches toacre-feet if the area units are acres. (The corresponding factor for metric unitsis 10-5 if the area units are hectares.) Each MASS-LINK table contains the set oftime series transfers that are to be associated with one or more of the linkagesin the SCHEMATIC block. The HSPF program combines the schematic linkages with themass time series transfers and automatically generates all of the necessary timeseries connections; these time series connections are automatically included in theNETWORK block by the program.
The example shown below illustrates the use of these blocks. In this example, thewatershed consists of three pervious land segments and two stream reaches. One ofthe land segments contributes loadings to both reaches. Loadings of flow,sediment, heat and one dissolved pesticide are being transferred from the land tothe stream, and the sediment loading from the land surface is assumed to consistof 10% sand, 35% silt and 55% clay. The SCHEMATIC and MASS-LINK blocks toaccomplish the required connections are shown below:
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********************************************************************** 1 2 3 4 5 6 71234567890123456789012345678901234567890123456789012345678901234567890**********************************************************************
SCHEMATIC <-Source-> <--Area--> <-Target-> MSLK *** <Name> # <-factor-> <Name> # Tbl# *** PERLND 1 200. RCHRES 1 1 PERLND 2 120. RCHRES 1 1 PERLND 2 235. RCHRES 2 1 PERLND 3 360. RCHRES 2 1 RCHRES 1 RCHRES 2 2 END SCHEMATIC MASS-LINK <Volume> <-Grp> <-Member-><--Mult--> <-Target> <-Grp> <-Member->***<Name> <Name> # #<-factor-> <Name> <Name> # #*** MASS-LINK 1 PERLND PWATER PERO 0.0833333 RCHRES INFLOW IVOL PERLND SEDMNT SOSED 1 0.10 RCHRES INFLOW ISED 1 PERLND SEDMNT SOSED 1 0.35 RCHRES INFLOW ISED 2 PERLND SEDMNT SOSED 1 0.55 RCHRES INFLOW ISED 3 PERLND PWTGAS POHT RCHRES INFLOW IHEAT PERLND PEST TOPST RCHRES INFLOW IDQUAL 1 END MASS-LINK 1 MASS-LINK 2RCHRES ROFLOW RCHRES INFLOW END MASS-LINK 2 END MASS-LINK
**********************************************************************
The SCHEMATIC block contains the global watershed linkages, i.e., PLS 1 providesloadings to Reach 1, PLS 2 provides loadings to Reaches 1 and 2, PLS 3 providesloadings to Reach 2, and Reach 1 is upstream of Reach 2. The areas of PLS's 1 and3 are 200 acres and 360 acres, respectively, and the area of PLS 2 is 355 acres,of which 120 acres are tributary to Reach 1 and 235 acres are tributary to Reach2.
The MASS-LINK block contains details of the individual time series connections thatneed to be specified for each of the watershed linkages. Each of the four PLS-to-Reach entries in the SCHEMATIC block refers to MASS-LINK Table 1, which containssix time series connections from the PLS to the Reach. The Reach 1-to-Reach 2entry refers to MASS-LINK Table 2; this table contains the ROFLOW-INFLOWconnection, which is automatically expanded by the program to generate allnecessary time series connections from one reach to another.
The time series connections in the MASS-LINK block are combined with the SCHEMATIClinkages to generate the full set of connections needed in the simulation. In this
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process, the program sets up a set of connections for each [SCHEMATIC entry]/[MASS-LINK table] pair. The multiplication factor for each connection is obtained bycombining the 'area' factor from the SCHEMATIC block and the 'units/otherconversion' factor from the MASS-LINK block. The explicit time series connectionsgenerated by HSPF and included in the NETWORK Block for this example are shownbelow:
NETWORK
**** PLS 1 to RCH 1PERLND 1 PWATER PERO 16.66 SAME RCHRES 1 INFLOW IVOL PERLND 1 SEDMNT SOSED 1 20. SAME RCHRES 1 INFLOW ISED 1 PERLND 1 SEDMNT SOSED 1 70. SAME RCHRES 1 INFLOW ISED 2 PERLND 1 SEDMNT SOSED 1 110. SAME RCHRES 1 INFLOW ISED 3 PERLND 1 PWTGAS POHT 200. SAME RCHRES 1 INFLOW IHEAT PERLND 1 PEST TOPST 200. SAME RCHRES 1 INFLOW IDQUAL 1
**** PLS 2 to RCH 1PERLND 2 PWATER PERO 10. SAME RCHRES 1 INFLOW IVOL PERLND 2 SEDMNT SOSED 1 12. SAME RCHRES 1 INFLOW ISED 1 PERLND 2 SEDMNT SOSED 1 42. SAME RCHRES 1 INFLOW ISED 2 PERLND 2 SEDMNT SOSED 1 66. SAME RCHRES 1 INFLOW ISED 3 PERLND 2 PWTGAS POHT 120. SAME RCHRES 1 INFLOW IHEAT PERLND 2 PEST TOPST 120. SAME RCHRES 1 INFLOW IDQUAL 1
**** PLS 2 to RCH 2PERLND 2 PWATER PERO 19.58 SAME RCHRES 2 INFLOW IVOL PERLND 2 SEDMNT SOSED 1 23.50 SAME RCHRES 2 INFLOW ISED 1 PERLND 2 SEDMNT SOSED 1 82.25 SAME RCHRES 2 INFLOW ISED 2 PERLND 2 SEDMNT SOSED 1 129.25 SAME RCHRES 2 INFLOW ISED 3 PERLND 2 PWTGAS POHT 235. SAME RCHRES 2 INFLOW IHEAT PERLND 2 PEST TOPST 235. SAME RCHRES 2 INFLOW IDQUAL 1
**** PLS 3 to RCH 2PERLND 3 PWATER PERO 30. SAME RCHRES 2 INFLOW IVOL PERLND 3 SEDMNT SOSED 1 36. SAME RCHRES 2 INFLOW ISED 1 PERLND 3 SEDMNT SOSED 1 126. SAME RCHRES 2 INFLOW ISED 2 PERLND 3 SEDMNT SOSED 1 198. SAME RCHRES 2 INFLOW ISED 3 PERLND 3 PWTGAS POHT 360. SAME RCHRES 2 INFLOW IHEAT PERLND 3 PEST TOPST 360. SAME RCHRES 2 INFLOW IDQUAL 1
**** RCH 1 to RCH 2 (HYDR, HTRCH, SEDTRN, and GQUAL sections are active)RCHRES 1 ROFLOW ROVOL 1.0 SAME RCHRES 2 INFLOW IVOL RCHRES 1 ROFLOW ROHEAT 1.0 SAME RCHRES 2 INFLOW IHEAT RCHRES 1 ROFLOW ROSED 1 1.0 SAME RCHRES 2 INFLOW ISED 1RCHRES 1 ROFLOW ROSED 2 1.0 SAME RCHRES 2 INFLOW ISED 2RCHRES 1 ROFLOW ROSED 3 1.0 SAME RCHRES 2 INFLOW ISED 3RCHRES 1 ROFLOW RODQAL 1.0 SAME RCHRES 2 INFLOW IDQAL 1RCHRES 1 ROFLOW ROSQAL 1 1 1.0 SAME RCHRES 2 INFLOW ISQAL 1 1RCHRES 1 ROFLOW ROSQAL 2 1 1.0 SAME RCHRES 2 INFLOW ISQAL 2 1RCHRES 1 ROFLOW ROSQAL 3 1 1.0 SAME RCHRES 2 INFLOW ISQAL 3 1END NETWORK
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4.6.4.1 SCHEMATIC Block In this block the user specifies the global linkages of land segments with streamreaches and between stream reaches. Each of these linkages is combined with thedetailed time series connections specified in one of the MASS-LINK tables togenerate a complete set of time series connections for the linkage. ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** SCHEMATIC<svol>< #> <-afact--> <tvol>< #> <ML#> .............................................................................Above line repeats until all network entries have been made .............................................................................END SCHEMATIC *******Example******* SCHEMATIC<-Source-> <--Mult--> <-Target > MSLK ***<Name> # <-factor-> <Name> # Tbl# ***PERLND 1 200. RCHRES 2 1PERLND 2 300. RCHRES 5 1RCHRES 4 1. RCHRES 5 2END SCHEMATIC ********************************************************************************
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Details --------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column -------------------------------------------------------------------------------- <svol> SVOL 1 A6 SVOL is the Operation-type of the source operation. < #> SVOLNO 7 I4 SVOLNO is the source Operation-type number (e.g., PERLND 5) <afact> AFACTR 29 F10.0 The area factor by which data from the source will be multiplied before being added to the target. This factor will be combined with the factor in the MASS-LINK Block. Default (blank field)= 1.0
<tvol> TVOL 44 A6 TVOL is the Operation-type of the target.
< #> TVOLNO 50 I4 TVOLNO is the target Operation-type number (e.g., RCHRES 5) <ml#> MSLKNO 57 I4 MASS-LINK table number that will be used to generate the NETWORK entries for this linkage.--------------------------------------------------------------------------------
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4.6.4.2 MASS-LINK Block In this block the user specifies those time series connections which will becombined with the linkages in the SCHEMATIC Block to generate a set of time seriesconnections in the NETWORK Block. ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** MASS-LINK
MASS-LINK #<svol> <sgrp> <smem><m#><-mfact--> <tvol> <tgrp> <tmem><m#> .............................................................................Above line repeats until all mass-link entries have been made ............................................................................. END MASS-LINK #
END MASS-LINK *******Example******* MASS-LINK MASS-LINK 1<-Volume-> <-Grp> <-Member-><--Mult--> <-Target vols> <-Grp> <-Member-> ***<Name> <Name> # #<-factor-> <Name> # # <Name> # # ***PERLND PWATER PERO 0.08333 RCHRES INFLOW IVOLPERLND SEDMNT SOSED RCHRES INFLOW ISED 1PERLND PQUAL POQUAL 1 RCHRES INFLOW OXIF 2 END MASS-LINK 1END MASS-LINK ********************************************************************************
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Details --------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column -------------------------------------------------------------------------------- <svol> SVOL 1 A6 SVOL is the Operation-type of the source operation. <sgrp> SGRPN 12 A6 Group to which the source time series belong(s). <smem> SMEMN 19 A6 Source member name. <m#> SMEMSB(2) 25 2A2 Source member name subscripts; may be 2-character CATEGORY tag if applicable; must be integer otherwise. See Time Series Catalog. <mfact> MFACTR 29 F10.0 The factor by which data from the source will be multiplied before being added to the target. Default (blank field)= 1.0 <tvol> TVOL 44 A6 TVOL is the Operation-type of the target. <tgrp> TGRPN 59 A6 Group to which the target time series belong(s). <tmem> TMEMN 66 A6 Target member name. <m#> TMEMSB(2) 72 2A2 Target member name subscripts; may be 2-character CATEGORY tag if applicable; must be integer otherwise. See Time Series Catalog.--------------------------------------------------------------------------------
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4.6.5 EXT TARGETS Block (External targets) In this block the user specifies those time series which will be output from theoperations in a RUN, to data sets in the WDM or DSS Files. If there are no suchtransfers the block may be omitted. ******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout****** EXT TARGETS<svol><o#> <sgrp> <smem><m#><-mfact--><tr> <tvol><t#> <extm>qf <ts> <ag> <am> .............................................................................Above line repeats until all external targets have been specified .............................................................................END EXT TARGETS *******Example******* EXT TARGETS<-Volume-> <-Grp> <-Member-><--Mult-->Tran <-Volume-> <Member> Tsys Aggr Amd ***<Name> # <Name> # #<-factor->strg <Name> # <Name>qf tem strg strg***RCHRES 6 GQUAL DQAL 3 1. AVER WDM4 25 CONC ENGL AGGR REPLPERLND 301 NITR PONITR SUM DSS 122 ENGL REPLEND EXT TARGETS ********************************************************************************
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Details --------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column--------------------------------------------------------------------------------<svol> SVOL 1 A6 SVOL is the Operation-type of the source operation.
<o#> SVOLNO 7 I4 SVOLNO is the source Operation-type number (e.g., PERLND 5)
<sgrp> SGRPN 12 A6 Group to which the source time series belong(s).
<smem> SMEMN 19 A6 Source member name.
<m#> SMEMSB(2) 25 2A2 Source member name subscripts; may be 2-character CATEGORY tag if applicable; must be integer otherwise. See Time Series Catalog.
<mfact> MFACTR 29 F10.0 The factor by which data from the source will be multiplied before being added to the target. Default (blank field)= 1.0
<tr> TRAN 39 A4 String indicating which transformation function to use in transferring time series from source to target. See Section 4.6.7 for defaults.
<tvol> TVOL 44 A6 External target volume. Valid values are WDMn (Watershed Data Management System file, where n
is 1-4 or blank) and DSS (Data Storage System). <t#> TVOLNO 50 I4 Data-set Number (if TVOL = WDMn, or DSS). <extm> TMEMN 55 A6 Data-set TSTYP attribute (if TVOL = WDMn). A4 (Blank if TVOL = DSS.)
qf QLFG 61 I2 Quality-of-data (if TVOL = WDM); specifies the quality tag to be attached to data placed in a WDM data set; valid values = 0 - 31; default = 0.
<ts> TSYST 64 A4 Unit system of data to be written to WDM or DSS data set; valid values = ENGL and METR; default = ENGL.<ag> AGGST 69 A4 String indicating whether the data should be aggregated when placed in a WDM data set having a time step greater than the source time step; valid value is AGGR; default is no aggregation.<am> AMDST 74 A4 String indicating how the target data set is to be accessed. Valid values are: ADD or REPL for a
WDM or DSS file. See below for explanation.------------------------------------------------------------------------------
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Explanation
This block is similar to the EXT SOURCES Block, but serves the opposite purpose.Thus, the entries have similar formats (but are reversed). In addition, each entryin the EXT TARGETS Block has the <am> field, which indicates how the target dataset will be accessed. The user should be aware of the differences between theseoptions when the target data set is in a WDM or DSS file. The valid values and themeaning of each are: ADD For a WDM data set, this option is designed to add data when no pre-
existing data are present for any period after the starting time of therun, including times after the time span of the run.
For a DSS data record, this option preserves pre-existing data before andafter the beginning of the run, and requires that no data pre-exist duringthe time span of the run.
REPL For a WDM data set, this option will result in the overwriting of any
existing data which follows the starting time of the run, including dataafter the time span of the run.
For a DSS data record, pre-existing data during the time span of the runis overwritten, but pre-existing data before and after the run arepreserved.
In summary, for a WDM data set, the ADD option is used to add data when nopre-existing data are present after the starting time of the run, while the REPLoption results in overwriting existing data, both during and after the time spanof the run.
Data placed in a WDM data set will normally have a time step equal to the time stepof the run, even if the data set has a different time step than the run . However,the user may optionally specify that aggregation occur if the target data-set timestep is an integer multiple (2 or greater) of the run time step. The time step ofa WDM data set is specified by the TCODE and TSSTEP attributes of the data set.Disaggregation is not permitted when placing data in WDM data sets.
For a DSS data record, only data during the actual time span of the run areaffected. The ADD option specifies that such data cannot pre-exist, while the REPLoption allows any pre-existing data to be overwritten.
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4.6.6 PATHNAMES Block
In this block the user associates data-set numbers with the time series to beaccessed from the DSS (HEC Data Storage System) files, and specifies the data typesof the time series.
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
PATHNAMES<ds>f# <ctype-> <---------------------------pathname---------------------------> .............................................................................Above line repeats until all external targets have been specified .............................................................................END PATHNAMES
*******Example*******
PATHNAMES<ds>f# <ctype-> <***************************pathname***************************> 41 1 PER-CUM /TEST/FARM COOP WS/EVAP//1DAY/OBSERVED-INCHES|DAY/END PATHNAMES
********************************************************************************
Details
--------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column--------------------------------------------------------------------------------<ds> DSSDSN 1 I4 DSSDSN is the temporary data-set number assigned to the DSS record(s) which make up the time series.f# DSSFL 5 I2 DSSFL is the index number for the DSS file containing these data record(s); it is assigned to each DSS file in the FILES block<ctype-> CTYPE 8 A8 Data type string for the data record(s). Valid values are: INST-VAL, PER-AVER, PER-CUM.pathname CPATH 17 A64 Pathname for DSS record(s). It is recommended that the D part be left empty, as it is generated by HSPF as needed.------------------------------------------------------------------------------
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Explanation
This section is required if any time series data are to be accessed from DSS files.In this section, unique ID numbers are assigned to "data sets" in the DSS file(s);these ID numbers are used in the EXT-SOURCES and EXT-TARGETS blocks to specify(i.e., identify) the data sets.
See Section 4.6.1.1 for further discussion of DSS concepts.
4.6.7 Time Series Transform Functions Whenever time series are transferred from a source to a target, a "transformation"takes place. The user can specify the transformation function in field <tr>; ifit is blank the default function is supplied. The range of permissible functionsis: --------------------------------------------------------------------------------Interval Source Target <------- Functions ---------> relation Type Type Defaults Others -------------------------------------------------------------------------------- SDELT = TDELT Point to Point SAME none Mean to Mean SAME none Point to Mean AVER none
SDELT > TDELT Point to Point INTP/AVER (a) none (b) Mean to Mean DIV SAME Point to Mean AVER none SDELT < TDELT Point to Point LAST/AVER (a) none Mean to Mean SUM AVER,MAX,MIN Point to Mean AVER SUM,MAX,MIN -------------------------------------------------------------------------------- Key: SDELT Time interval of source time series TDELT Time interval of target time series (a) Second default keyword applies to WDM source time series and all DSS external time series. (b) This interval relation is invalid for WDM target; i.e., output disaggregation is not permitted to WDM data setsNotes: 1. See below (Note 2 and next page) for explanations of the transform keywords.
2. For WDM data sets, TDELT and SDELT refer to the time step defined by the WDMattributes TCODE and TSSTEP; however, data may be stored in the data set atother time steps.
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3. For WDM or DSS data sets, the functions AVER and SAME imply either AVER orSAME; and the functions SUM and DIV imply either SUM or DIV, whichever isappropriate for the actual time step of the data.
4. The type of WDM data sets is determined by the attribute TSFORM. If TSFORM =1 or 2, the type is MEAN; if TSFORM = 3, the type is POINT.
5. The type of sequential (SEQ) time series is not defined; consequently, thesetime series are assumed to have the same type as the target time series.
6. The type of DSS data records is determined from the data type string. (SeeSection 4.6.1.1 above for discussion.) If none is specified, the time seriesis assumed to be mean-valued.
7. Keywords less than 4 characters long must be left-justified in the field.
8. For further information, see Appendix V and Time Series Catalog (Section 4.7of this part).
The time series transform functions given above are completed before themultiplication factor given in the EXTERNAL SOURCES, EXTERNAL TARGETS and NETWORKblocks are applied. These transform functions are defined as follows: AVER Compute the integral of the source time series over each target time step,
divide by the target time step and assign the value to the time step in thetarget time series. See Appendix V for definition of the integral of a timeseries.
DIV Divide each mean value of the source time series by the ratio of the source
time step to the target time step and assign the results to each of thetarget time steps contained in the source time step.
INTP Interpolate linearly between adjacent point values in the source time
series and assign the interpolated values to each time point in the targettime series.
LAST Take the value at the last time point of the source time series which
belongs to the time step of the target time series and assign the value tothe time step of the target time series. See Appendix V for a definitionof the meaning of "belonging".
MAX Find the maximum value of the source time series for all points belonging
to the target time step (point-value time series) or find the maximum valueof the source time series for all time steps contained within the targettime step (mean-value time series). Assign the maximum value to the timestep of the target time series. The definition of "belonging" (given inAppendix V) was motivated by the desire to make MAX and MIN unique forpoint-value time series.
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MIN Find the minimum value of the source time series for all points belongingto the target time step (point-value time series) or find the minimum valueof the source time series for all time steps contained within the targettime series (mean-value time series). Assign the minimum value to the timestep of the target time series.
SAME Take the value at each time step or time point of the source time series
and assign the value to the corresponding time point (point-value timeseries), the corresponding time step (mean-value time series), or all thecontained time steps (mean-value time series with time step less than thesource time step) of the target time series.
SUM For point-value source time series: Compute the sum of the values for all
points in the source time series belonging to the target series time stepplus the value of the source time series at the initial point of the targettime step and assign the sum to the target time step. For mean- valuesource time series: Compute the sum of the values for all time steps in thesource time series contained within the target series time step and assignthe sum to the target time step.
4.6.8 Warning 1. In this block it is not permissible to route several sources to the same
external target. If you want to combine several time series and write theresult to an external target, first use a utility operation (COPY) to combinethe data, and then use this block to route the result to the external target.
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4.7 Time Series Catalog This section documents all the time series which are required by, and which can beoutput by, all the operating modules in the HSPF system. The time series are arranged in groups. Thus, to specify an operation associatedtime series in the EXT SOURCES, NETWORK or EXT TARGETS Blocks, the user suppliesa group name followed, optionally, by a member name and subscripts. The time series documented in this section can be separated into three categories: 1. Input only. Some time series can only be input to their operating module (e.g.
member PREC of group EXTNL in module PERLND).
2. Input or output. Some time series can either be input to their operatingmodule or output from it, depending on the options in effect. For example, ifsnow accumulation and melt on a Pervious Land-segment (PLS) is being simulatedin a given RUN, time series WYIELD in group SNOW can be output to the WDM file.Then, if section SNOW is inactive but section PWATER is active in a subsequentRUN, the same time series WYIELD may be specified as an input to the PERLNDmodule. This feature makes it possible to calibrate an application module inan incremental manner. First, the outputs from section 1 are calibrated to thefield data; then the outputs from section 2 are calibrated using outputs fromsection 1 as inputs, etc. Sections calibrated in earlier runs need not bere-run if the needed outputs from them have been stored.
3. Output only. Some time series can be computed by and output from their
operating module, but never serve as inputs to it (e.g. member ALBEDO of groupSNOW in module PERLND).
To run an operating module, the user must ensure that all the input time serieswhich it requires are made available to it. This is done by making appropriateentries in the EXT SOURCES or NETWORK blocks. To ascertain which time series arerequired, one should consult the Time Series Catalog for the appropriate module.For example, assume that sediment production and washoff/scour from a PLS are beingsimulated using the snow and water budget results from a previous RUN. In thisscenario, section SEDMNT would be active, but sections ATEMP, SNOW and PWATER wouldnot be active. Then, Table 4.7(1).5 shows the following:
1. member (time series) PREC of group EXTNL is a required input time series(member SLSED is optional)
2. members RAINF and SNOCOV of group SNOW are required inputs, because sectionSNOW is inactive
3. members SURO and SURS of group PWATER are required inputs, because sectionPWATER is inactive
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The user can obtain further details on the above time series by consulting thetable for the appropriate group (e.g. Table 4.7(1).1 for group EXTNL).Table 4.7(1).5 shows which time series are computed in the SEDMNT section of thePERLND module and may therefore be output (members DETS through SOSDB). Thus, in the EXT SOURCES and/or NETWORK blocks, entries must appear which specifymembers PREC, RAINF, etc (groups EXTNL, SNOW, PWATER) as targets to which sourcetime series are routed. Also, in the NETWORK and/or EXT TARGETS blocks, entriesmay appear which specify one or more of members DETS through SOSDB (of groupSEDMNT) as source time series, which are routed to other operations or to the WDM. The tables which follow are otherwise self explanatory, except for the abbreviation"ivld" which appears frequently in the "Units" fields. It means "interval of thedata" (to distinguish it from the internal, or simulation interval). Thus, if aWDM or DSS data set containing 1-hour precipitation data is input to an operationwith a DELT of two hours, ivld is 1 hour.
4.7.1 Connection of Surface and Instream Application Modules
In HSPF, the operational connection between the land surface and instreamsimulation modules is accomplished through the NETWORK Block and/or theSCHEMATIC/MASS-LINK Blocks. Time series of runoff, sediment, and pollutantloadings generated on the land surface are passed to the receiving stream forsubsequent transport and transformation instream. This connection of the IMPLNDand/or PERLND modules with the RCHRES module requires explicit definition ofcorresponding time series in the linked modules. A one-to-one correspondenceexists between several land segment outflow time series and corresponding streamreach inflow time series (e.g. runoff, sediment, dissolved oxygen, etc.); howeverin order to maintain flexibility, some of the time series are more general, and nounique correspondence exists. Also, in some cases, a process or material simulatedin the stream will have no corresponding land surface quantity. For example, theinflow of plankton to a stream occurs only from upstream reaches and not from aland segment.
4.7.2 Atmospheric Deposition of Water Quality Constituents
Input time series are available in HSPF to aid in the simulation of atmosphericdeposition of quality constituents. Atmospheric deposition inputs can be specifiedin two possible ways depending on the form of the available data. If thedeposition is in the form of a flux (mass per area per time), then it is considered"dry deposition". If the deposition is in the form of a concentration in rainfall,then it is considered "wet deposition", and the program automatically combines itwith the input rainfall time series to compute the resulting flux. Either type ofdeposition data can be input as a time series, which covers the entire simulationperiod, or alternatively, as a set of monthly values that is used for each year ofthe simulation. The specific atmospheric deposition time series for eachoperational module (PERLND, IMPLND, RCHRES) are documented in the EXTNL table ofthe Time Series Catalog for that module.
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An additional use of these atmospheric deposition time series is the specificationof agricultural chemical and fertilizer inputs to the soil. These input timeseries thus provide an alternative to the SPEC-ACTIONS block as a means forchanging soil storages of chemicals in the AGCHEM sections of the program. Forthis purpose, "deposition" to the upper soil layer in addition to the surface soillayer is available in PERLND sections NITR, PHOS, and TRACER. (Section PEST hastime series only for the surface layer, since pesticides are not normallyincorporated into the soil as are fertilizers.) Depending on the complexity of theagricultural practices being modeled, the user should decide whether the SPEC-ACTIONS or the time series inputs are simpler to construct.
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4.7(1) Catalog for PERLND module The time series groups associated with this module are shown in Figure 4.7(1)-1. The members contained within each group are documented in the following tables.
4.7(1).1 Group EXTNL ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series always external (input only) to module PERLND: GATMP 1 1 - Deg F Deg C Measured air temperaturePREC 1 1 - in/ivld mm/ivld Measured precipitationDTMPG 1 1 - Deg F Deg C Measured dewpoint temperatureWINMOV 1 1 - mi/ivld km/ivld Measured wind movementSOLRAD 1 1 - Ly/ivld Ly/ivld Measured solar radiationCLOUD 1 1 - tenths tenths Cloud cover (range: 0 - 10) PETINP 1 1 - in/ivld mm/ivld Input potential E-T SURLI 1 1 - in/ivld mm/ivld Surface lateral inflowIFWLI 1 1 - in/ivld mm/ivld Interflow lateral inflowAGWLI 1 1 - in/ivld mm/ivld Active groundwater lateral inflow SLSED 1 1 - tons/ tonnes/ Lateral input of sediment ac.ivld ha.ivldPQADFX NQ 1 - qty/ qty/ Dry or total atmospheric deposition ac.ivld ha.ivld of QUALOFPQADCN NQ 1 - qty/ft3 qty/l Concentration of QUALOF in rain for wet atmospheric depositionPEADFX NPST 3 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of pesticide. The first subscript indicates the pesticide; the second indicates the species: crystalline, adsorbed, or solutionPEADCN NPST 3 - mg/l mg/l Concentration of pesticide in rain for wet atmospheric deposition. Subscripts same as above.NIADFX 3 2 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of nitrogen. First subscript indi- cates species: NO3, NH3, organic N; the second subscript indicates the affected soil layer: surface or upper.NIADCN 3 2 - mg/l mg/l Concentration of nitrogen in rain for wet atmospheric deposition. Subscripts as above.
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PHADFX 2 2 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of phosphorus. The first subscript indicates species: PO4, organic P; the second subscript indicates the affected soil layer (see above)PHADCN 2 2 - mg/l mg/l Concentration of phosphorus in rain for wet atmospheric deposition. Subscripts as above.TRADFX 2 1 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of tracer substance. Subscript in- dicates affected soil layer (see above)TRADCN 2 1 - mg/l mg/l Concentration of tracer in rain. Subscript as above.-------------------------------------------------------------------------------
4.7(1).2 Group ATEMP ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section ATEMP: AIRTMP 1 1 - Deg F Deg C Estimated surface air temperature over the Land-segment Input time series required to compute the above: Group EXTNL always required GATMP gage air temperature PREC precipitation-------------------------------------------------------------------------------
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Figure 4.7(1)-1 Groups of time series associated with the PERLND Module
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4.7(1).3 Group SNOW ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section SNOW: PACK 1 1 * in mm Total contents of pack (water equiv)PACKF 1 1 * in mm Frozen contents of pack, ie. snow + ice (water equivalent) PACKW 1 1 * in mm Liquid water in packPACKI 1 1 * in mm Ice in pack (water equivalent) PDEPTH 1 1 * in mm Pack depthRDENPF 1 1 * none none Relative density of frozen contents of pack (PACKF/PDEPTH)SNOCOV 1 1 * none none Fraction of Land-segment covered by packALBEDO 1 1 * none none Albedo of the packPAKTMP 1 1 * Deg F Deg C Mean temperature of the pack SNOWF 1 1 - in/ivld mm/ivld Snowfall, water equivalentSNOWE 1 1 - in/ivld mm/ivld Evaporation from PACKF (sublimation), water equivalentWYIELD 1 1 - in/ivld mm/ivld Water yielded by the pack (released to the land-surface)MELT 1 1 - in/ivld mm/ivld Quantity of melt from PACKF (water equivalent) RAINF 1 1 - in/ivld mm/ivld Rainfall Input time series required to compute the above:
Group EXTNL always required PREC precipitation DTMPG dewpoint temperature WINMOV wind movement SOLRAD solar radiation Group ATEMP required if section ATEMP inactive AIRTMP air temperature -------------------------------------------------------------------------------
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4.7(1).4 Group PWATER ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section PWATER: Land-segment-wide values: PERS 1 1 * in mm Total water stored in the PLS CEPS 1 1 * in mm Interception storageSURS 1 1 * in mm Surface (overland flow) storage UZS 1 1 * in mm Upper zone storageIFWS 1 1 * in mm Interflow storage LZS 1 1 * in mm Lower zone storageAGWS 1 1 * in mm Active groundwater storageRPARM 1 1 - in/ivld mm/ivld Current value of maximum lower zone E-T opportunity SURO 1 1 - in/ivld mm/ivld Surface outflow IFWO 1 1 - in/ivld mm/ivld Interflow outflow AGWO 1 1 - in/ivld mm/ivld Active groundwater outflowPERO 1 1 - in/ivld mm/ivld Total outflow from PLSIGWI 1 1 - in/ivld mm/ivld Inflow to inactive (deep) ground- water PET 1 1 - in/ivld mm/ivld Potential E-T, adjusted for snow cover and air temperatureCEPE 1 1 - in/ivld mm/ivld Evap. from interception storageUZET 1 1 - in/ivld mm/ivld E-T from upper zone LZET 1 1 - in/ivld mm/ivld E-T from lower zone AGWET 1 1 - in/ivld mm/ivld E-T from active groundwater storage BASET 1 1 - in/ivld mm/ivld E-T taken from active groundwater outflow (baseflow)TAET 1 1 - in/ivld mm/ivld Total simulated E-T IFWI 1 1 - in/ivld mm/ivld Interflow inflow (excluding any lateral inflow) UZI 1 1 - in/ivld mm/ivld Upper zone inflow INFIL 1 1 - in/ivld mm/ivld Infiltration to the soilPERC 1 1 - in/ivld mm/ivld Percolation from upper to lower zoneLZI 1 1 - in/ivld mm/ivld Lower zone inflow AGWI 1 1 - in/ivld mm/ivld Active groundwater inflow (excluding any lateral inflow) SURI 1 1 - in/ivld mm/ivld Surface inflow (including any lateral inflow) -------------------------------------------------------------------------------
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Input time series required to compute the above: Group EXTNL SURLI optional IFWLI optional AGWLI optional PETINP PREC required if snow not considered (CSNOFG= 0) Group ATEMP AIRTMP only required if section ATEMP is inactive and CSNOFG= 1
Group SNOW only required if section SNOW is RAINF inactive and snow is considered SNOCOV (CSNOFG= 1) WYIELD PACKI only required if ICEFG= 1
Group PSTEMP only required if section PSTEMP LGTMP is inactive and IFFCFG= 2-------------------------------------------------------------------------------
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4.7(1).5 Group SEDMNT
------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section SEDMNT:
Land-segment-wide values: DETS 1 1 * tons/ac tonnes/ha Storage of detached sedimentSTCAP 1 1 * tons/ tonnes/ Sediment transport capacity ac.ivld ha.ivld by surface runoff WSSD 1 1 - tons/ tonnes/ Washoff of detached sediment ac.ivld ha.ivldSCRSD 1 1 - tons/ tonnes/ Scour of matrix (attached) soil ac.ivld ha.ivldSOSED 1 1 - tons/ tonnes/ Total removal of soil and sediment ac.ivld ha.ivldDET 1 1 - tons/ tonnes/ Quantity of sediment detached from ac.ivld ha.ivld soil matrix by rainfall impact
Input time series required to compute the above:
Group EXTNL always required PREC SLSED optional Group SNOW only required if section SNOW RAINF is inactive and snow is considered SNOCOV (CSNOFG= 1) Group PWATER only required if section PWATER SURO is inactive SURS-------------------------------------------------------------------------------
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4.7(1).6 Group PSTEMP ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section PSTEMP: AIRTC 1 1 - Deg F Deg C Air temperature on the PLS SLTMP 1 1 - Deg F Deg C Surface layer soil temperature ULTMP 1 1 - Deg F Deg C Upper layer soil temperatureLGTMP 1 1 - Deg F Deg C Lower and groundwater layer soil temperature Input time series required to compute the above: Group ATEMP only required if section ATEMP is AIRTMP inactive------------------------------------------------------------------------------- 4.7(1).7 Group PWTGAS ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section PWTGAS: SOTMP 1 1 * Deg F Deg C Temperature of surface outflow IOTMP 1 1 * Deg F Deg C Temperature of interflow outflow AOTMP 1 1 * Deg F Deg C Temperature of active groundwater outflowSODOX 1 1 * mg/l mg/l DO concentration in surface outflowSOCO2 1 1 * mg/l mg/l CO2 concentration in surface outflow IODOX 1 1 * mg/l mg/l DO concentration in interflow outflowIOCO2 1 1 * mg/l mg/l CO2 concentration in interflow outflow AODOX 1 1 * mg/l mg/l DO concentration in active groundwater outflow -------------------------------------------------------------------------------
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(continued)------------------------------------------------------------------------------- AOCO2 1 1 * mg/l mg/l CO2 concentration in active groundwater outflow SOHT 1 1 - BTU/ kcal/ Heat energy in surface outflow ac.ivld ha.ivld (relative to freezing point)IOHT 1 1 - BTU/ kcal/ Heat energy in interflow outflow ac.ivld ha.ivldAOHT 1 1 - BTU/ kcal/ Heat energy in active groundwater ac.ivld ha.ivld outflow POHT 1 1 - BTU/ kcal/ Heat energy in total outflow from ac.ivld ha.ivld PLS SODOXM 1 1 - lb/ kg/ Flux of DO in surface outflow ac.ivld ha.ivldSOCO2M 1 1 - lb/ kg/ Flux of CO2 in surface outflow ac.ivld ha.ivldIODOXM 1 1 - lb/ kg/ Flux of DO in interflow outflow ac.ivld ha.ivldIOCO2M 1 1 - lb/ kg/ Flux of CO2 in interflow outflow ac.ivld ha.ivldAODOXM 1 1 - lb/ kg/ Flux of DO in active groundwater ac.ivld ha.ivld outflow AOCO2M 1 1 - lb/ kg/ Flux of CO2 in active groundwater ac.ivld ha.ivld outflow PODOXM 1 1 - lb/ kg/ DO in total outflow from PLS ac.ivld ha.ivldPOCO2M 1 1 - lb/ kg/ CO2 in total outflow from PLS ac.ivld ha.ivld-------------------------------------------------------------------------------
Input time series required to compute the above:
Group SNOW only required if section SNOW is WYIELD inactive and snow is considered (CSNOFG= 1)
Group PWATER only required if section PWATER is SURO inactive IFWO AGWO
Group PSTEMP only required if section PSTEMP SLTMP is inactive ULTMP LGTMP-------------------------------------------------------------------------------
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4.7(1).8 Group PQUAL
------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------
Time series computed by module section PQUAL: SQO NQOF 1 * qty/ac qty/ha Storage of QUALOF on the surfacePQADDR NQ 1 - qty/ qty/ Dry or total atmospheric deposition ac.ivld ha.ivld of QUALPQADWT NQ 1 - qty/ qty/ Wet deposition of QUAL ac.ivld ha.ivldWASHQS NQSD 1 - qty/ qty/ Removal of QUALSD by association ac.ivld ha.ivld with detached sediment washoffSCRQS NQSD 1 - qty/ qty/ Removal of QUALSD by association ac.ivld ha.ivld with scour of matrix soilSOQS NQSD 1 - qty/ qty/ Total flux of QUALSD from surface ac.ivld ha.ivldSOQO NQOF 1 - qty/ qty/ Washoff of QUALOF from surface ac.ivld ha.ivldSOQUAL NQ 1 - qty/ qty/ Total outflow of QUAL from the ac.ivld ha.ivld surface IOQUAL NQIF 1 - qty/ qty/ Outflow of QUAL in interflow ac.ivld ha.ivld (QUALIF)AOQUAL NQGW 1 - qty/ qty/ Outflow of QUAL in active ground- ac.ivld ha.ivld water outflow (QUALGW)POQUAL NQ 1 - qty/ qty/ Total flux of QUAL from the PLS ac.ivld ha.ivldSOQOC NQOF 1 - qty/ft3 qty/l Concentration of QUALOF in surface outflow SOQC NQ 1 - qty/ft3 qty/l Concentration of QUAL (QUALSD+ QUALOF) in surface outflow POQC NQ 1 - qty/ft3 qty/l Concentration of QUAL (total) in total outflow from PLS Input time series required to compute the above:
Group EXTNL PQADFX only required if dry or total atmospheric deposition is being simulated PQADCN only required if wet atmospheric PREC deposition is being simulated
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Group PWATER only required if PWATER is inactive SURO only required if one or more QUALs are QUALOFs, or if SOQC is required for one or more QUALs IFWO only required if one or more QUALs are QUALIFs AGWO only required if one or more QUALs are QUALGWs PERO only required if POQC is required for one or more QUALs
Group SEDMNT only required if section SEDMNT WSSD is inactive and one or more QUALs SCRSD are QUALSD's-------------------------------------------------------------------------------
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4.7(1).9 Group MSTLAY --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/commentName values n 1 2 d Engl Metr-------------------------------------------------------------------------------- Time series computed by module section MSTLAY: MST 5 1 * lb/ac kg/ha Water in surface, upper principal, upper auxiliary, lower, and groundwater storages FRAC 8 1 * /ivl /ivl Fractional fluxes through soil: FSO,FSP,FII,FUP,FIO,FLP,FLDP,FAO Input time series required to compute the above: Group PWATER: only required if section PWATER SURI,LZS,IGWI,AGWI,AGWS,AGWO, is inactive SURS,SURO,INFIL,IFWI,UZI,UZS, PERC,IFWS,IFWO
--------------------------------------------------------------------------------
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4.7(1).10 Group PEST
--------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr --------------------------------------------------------------------------------
Time series computed by module section PEST:
SPS 3 NPST * lb/ac kg/ha Amount of pesticide in surface storage UPS 3 NPST * lb/ac kg/ha Amount of pesticide in upper principal storage IPS NPST 1 * lb/ac kg/ha Amount of pesticide in upper auxiliary (interflow) storage LPS 3 NPST * lb/ac kg/ha Amount of pesticide in lower layer storage APS 3 NPST * lb/ac kg/ha Amount of pesticide in active groundwater layer storage TPS 3 NPST * lb/ac kg/ha Total amount of pesticide in the soilNote: SPS,UPS,LPS,APS and TPS give the storage of each pesticide by species. Thefirst subscript indicates the species: crystalline, adsorbed, or solution, Thesecond indicates the pesticide. For example, UPS(2,3) is the quantity of adsorbedpesticide in the upper layer principal storage, for the 3rd pesticide. The secondsubscript for IPS has a maximum value of one because only solution pesticide ismodeled in the upper layer auxiliary (interflow) layer. TOTPST NPST 1 * lb/ac kg/ha Total amount of pesticide in the soil (sum of all species).PEADDR NPST 3 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of pesticide. The second subscript indicates the species: crystalline, adsorbed, or solution.PEADWT NPST 3 - lb/ kg/ Wet deposition of pesticide form. ac.ivld ha.ivld Subscripts as above.SDPS 2 NPST - lb/ kg/ Outflow of sediment-associated ac.ivld ha.ivld pesticide (SDPSY and SDPSA for each pesticide)TSPSS 5 NPST - lb/ kg/ Fluxes of solution pesticide for ac.ivld ha.ivld the topsoil layers: SOPSS,SPPSS, UPPSS,IIPSS,IOPSS SSPSS 3 NPST - lb/ kg/ Fluxes of solution pesticide for ac.ivld ha.ivld the subsoil layers: LPPSS,LDPPSS, AOPSS
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SDEGPS NPST 1 - lb/ kg/ Amt. of degradation in surface layer ac.ivld ha.ivldUDEGPS NPST 1 - " " Amount of degradation in upper layerLDEGPS NPST 1 - " " Amount of degradation in lower layerADEGPS NPST 1 - " " Amount of degradation in groundwaterTDEGPS NPST 1 - " " Total amount of degradation in soil SOSDPS NPST 1 - " " Total outflow of sediment-associated pesticide (SDPSY + SDPSA) POPST NPST 1 - " " Total outflow of solution pesticide from the PLSTOPST NPST 1 - " " Total outflow of pesticide from the PLS Note: The subscript with maximum value NPST selects the particular pesticide. For example, POPST(2,1) is the outflow from the PLS of the second pesticide (in solution). Input time series required to compute the above: Group EXTNL PEADFX only required if dry or total atmospheric deposition is being simulated PEADCN only required if wet atmospheric PREC deposition is being simulated
Group SEDMNT only required if section SEDMNT SOSED is inactive Group PSTEMP only required if section PSTEMP SLTMP is inactive and ADOPFG = 1 ULTMP LGTMP Group MSTLAY only required if section MSTLAY MST is inactive FRAC--------------------------------------------------------------------------------
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4.7(1).11 Group NITR --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr --------------------------------------------------------------------------------Time series computed by module section NITR:
AGPLTN 1 1 * lb/ac kg/ha N in above-ground plant storageLITTRN 1 1 * " " N in litter storage
The above time series are available only if ALPNFG= 1
SN 8 1 * lb/ac kg/ha N in surface layer storageUN 8 1 * " " N in upper layer principal storageLN 8 1 * " " N in lower layer storageAN 8 1 * " " N in groundwater layer storageTN 8 1 * " " Total N in soil, by species In the above, the first subscript selects the species of N: 1 means particulatelabile organic N, 2 means adsorbed ammonium, 3 means solution ammonium, 4 meansnitrate, 5 means plant N, 6 means solution labile organic N, 7 means particulaterefractory organic N, 8 means solution refractory organic N IN 4 1 * lb/ac kg/ha N in upper layer auxiliary (interflow) storage In the above, the first subscript selects the species of N: 1 means solutionammonium, 2 means nitrate, 3 means solution labile organic N, 4 means solutionrefractory organic N (only soluble species are modelled in this storage) TOTNIT 1 1 * lb/ac kg/ha Total N stored in the PLS (all species)
NDFCT 5 1 * lb/ac kg/ac Deficit in yield-based plant uptake of N from the each soil layer: 1-surface, 2-upper, 3-lower, 4-active groundwater, 5-total
NUPTG 4 1 - lb/ kg/ Yield-based plant uptake target for ac.ivld ha.ivld N from each soil layer: 1-surface, 2-upper, 3-lower, 4-active groundwater
Catalog for PERLND Module
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NIADDR 3 2 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of nitrogen. The first subscript indicates the species: NO3, NH3, organic N; the second subscript indicates the affected soil layer: surface or upper.NIADWT 3 2 - " " Wet atmospheric deposition of nitrogen. Subscripts as above.
SEDN 3 1 - " " Outflows of sediment-associated N In the above, the first subscript selects the flux: 1 means labile organic Nremoval, 2 means adsorbed ammonium removal, 3 means refractory organic N removal.
SOSEDN 1 1 - lb/ kg/ Total outflow of sediment-associated ac.ivld ha.ivld N (organic N + adsorbed ammonium)
TSAMS 5 1 - " " Fluxes of solution ammonium in the topsoil TSNO3 5 1 - " " Fluxes of nitrate in the topsoilTSSLN 5 1 - " " Fluxes of solution labile organic N in the topsoil TSSRN 5 1 - " " Fluxes of solution refractory organic N in the topsoil
In the above, the first subscript selects the flux:1 means outflow with surface water outflow2 means percolation from surface to upper layer principal storage 3 means percolation from upper layer principal storage to lower layer storage 4 means flow from upper layer principal to upper layer auxiliary (interflow) storage5 means outflow from PLS with water from upper layer auxiliary (interflow) storage
SSAMS 3 1 - lb/ac.ivld kg/ha.ivld Fluxes of solution ammonium in the subsoil SSNO3 3 1 - " " Fluxes of nitrate in the subsoilSSSLN 3 1 - " " Fluxes of solution labile organic N in the subsoil SSSRN 3 1 - " " Fluxes of solution refractory organic N in the subsoil
In the above, the first subscript selects the flux: 1 means percolation from the lower layer to the active groundwater storage2 means deep percolation, from the lower layer to inactive groundwater3 means outflow from the PLS with water from the active groundwater storage
Catalog for PERLND Module
687
PONO3 1 1 - lb/ac.ivld kg/ha.ivld Total outflow of NO3 from the PLS PONH4 1 1 - " " Total outflow of NH4 from the PLSPOORN 1 1 - " " Total outflow of ORGN from the PLSPONITR 1 1 - " " Total outflow of N (NO3+NH4+ORGN) from the PLS.
TDENIF 1 1 - " " Total denitrification in the PLS
NFIXFX 5 1 - " " N fixation flux to the PLSAMVOL 5 1 - " " Ammonia volatilization from the PLSIn the above, the first subscript selects soil layer: 1-surface, 2-upper, 3-lower,4-active groundwater, 5-total
Input time series required to compute the above:
Same as for section PEST, except NIADFX and NIADCN are required for atmosphericdeposition. An input time series need only be supplied if section PEST and thesection which computes it (SEDMNT, PSTEMP or MSTLAY) are inactive.--------------------------------------------------------------------------------
Catalog for PERLND Module
688
4.7(1).12 Group PHOS
--------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Pame values n 1 2 d Engl Metr --------------------------------------------------------------------------------
Time series computed by module section PH0S:
SP 4 1 * lb/ac kg/ha P in surface layer storageUP 4 1 * " " P in upper layer principal storageLP 4 1 * " " P in lower layer storageAP 4 1 * " " P in groundwater layer storageTP 4 1 * " " Total P in soil, by species In the above, the first subscript selects the species of P: 1 means organic P, 2 means adsorbed phosphate, 3 means solution phosphate, 4 meansplant P derived from this layer IP 1 1 * lb/ac kg/ha P in upper layer auxiliary storage (interflow) (solution PO4)(only soluble species are modeled in this storage) TOTPHO 1 1 * lb/ac kg/ha Total P stored in the PLS (all species)
SPDFC 1 1 * lb/ac kg/ac Deficit in yield-based plant uptake of P from the surface soil layer
PDFCT 5 1 * lb/ac kg/ac Deficit in yield-based plant uptake of P from the each soil layer: 1-surface, 2-upper, 3-lower, 4-active groundwater, 5-total
PUPTG 4 1 - lb/ kg/ Yield-based plant uptake target for ac.ivld ha.ivld P from each soil layer: 1-surface, 2-upper, 3-lower, 4-active groundwater
PHADDR 2 2 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of phosphorus. The first subscript indicates the species: PO4 or organic P; the second subscript indicates the affected soil layer: surface or upper.PHADWT 2 2 - lb/ kg/ Wet atmospheric deposition of ac.ivld ha.ivld phosphorus. Subscripts as above.
Catalog for PERLND Module
689
SEDP 2 1 - lb/ac.ivld kg/ha.ivld Outflows of sediment-associated P In the above, the first subscript selects the flux: 1 means organic P removal,2 means adsorbed phosphate removal SOSEDP 1 1 - lb/ac.ivld kg/ha.ivld Total outflow of sediment-associated P (organic P + adsorbed phosphate)
TSP4S 5 1 - lb/ac.ivld kg/ha.ivld Fluxes of solution phosphate in the in the topsoil.
In the above, the first subscript selects the flux: 1 means outflow with surface water outflow2 means percolation from surface to upper layer principal storage 3 means percolation from upper layer principal storage to lower layer storage 4 means flow from upper layer principal to upper layer auxiliary (interflow) storage5 means outflow from PLS with water from upper layer auxiliary (interflow) storage SSP4S 3 1 - lb/ac.ivld kg/ha.ivld Fluxes of solution phosphate in the subsoil.
In the above, the first subscript selects the flux:1 means percolation from the lower layer to the active groundwater storage2 means deep percolation, from the lower layer to inactive groundwater3 means outflow from the PLS with water from the active groundwater storage POPHOS 1 1 - lb/ac.ivld kg/ha.ivld Total outflow of P from the PLS. Input time series required to compute the above: Same as for section PEST, except PHADFX and PHADCN are required for atmosphericdeposition. An input time series need only be supplied if sections PEST and NITRand the module section which computes it (SEDMNT, PSTEMP or MSTLAY) are inactive.--------------------------------------------------------------------------------
Catalog for PERLND Module
690
4.7(1).13 Group TRACER --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr --------------------------------------------------------------------------------
Time series computed by module section TRACER:
STRSU 1 1 * lb/ac kg/ha Tracer in surface layer storage UTRSU 1 1 * " " Tracer in upper principal storageITRSU 1 1 * " " Tracer in upper auxiliary storageLTRSU 1 1 * " " Tracer in lower layer storage ATRSU 1 1 * " " Tracer in groundwater layer storage TRSU 1 1 * " " Total tracer stored in the PLSTRADDR 2 1 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of tracer. The subscript indicates the soil layer: surface or upper.TRADWT 2 1 - " " Wet atmospheric deposition of tracer. Subscripts as above.TSTRS 5 1 - lb/ac.ivld kg/ha.ivld Fluxes of tracer in topsoil In the above, the first subscript indicates the flux: 1 means outflow with surface water outflow2 means percolation from surface to upper layer principal storage 3 means percolation from upper layer principal to lower layer storage 4 means flow from upper principal to upper auxiliary (interflow) storage5 means outflow from the PLS from upper layer transitory (interflow) storage SSTRS 3 1 - lb/ac.ivld kg/ha.ivld Fluxes of tracer in subsoil
In the above, the first subscript indicates the flux: 1 means percolation from lower layer to active groundwater storage2 means deep percolation, from lower layer to inactive groundwater3 means outflow from the PLS from the active groundwater storage
POTRS 1 1 - lb/ac.ivld kg/ha.ivld Total outflow of tracer from the PLS
Catalog for PERLND Module
691
Input time series required to compute the above:
Group EXTNL TRADFX only required if dry or total atmospheric deposition is being simulated TRADCN only required if wet atmospheric PREC deposition is being simulated
Group MSTLAY only required if MSTLAY, PEST, NITR MST and PHOS are all inactive; else FRAC these time series will already have been supplied--------------------------------------------------------------------------------
Catalog for IMPLND Module
692
4.7(2) Catalog for IMPLND module
The time series groups associated with this application module are shown in Figure4.7(2)-1. The members contained within each group are documented in the tableswhich follow.
4.7(2).1 Group EXTNL
------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------
Time series always external (input only) to module IMPLND: GATMP 1 1 - Deg F Deg C Measured air temperature PREC 1 1 - in/ivld mm/ivld Measured precipitationDTMPG 1 1 - Deg F Deg C Measured dewpoint temperatureWINMOV 1 1 - mi/ivld km/ivld Measured wind movementSOLRAD 1 1 - Ly/ivld Ly/ivld Measured solar radiationCLOUD 1 1 - tenths tenths Cloud cover (range: 0 - 10) PETINP 1 1 - in/ivld mm/ivld Input potential E-T SURLI 1 1 - in/ivld mm/ivld Surface lateral inflowSLSLD 1 1 - tons/ tonnes/ Lateral input of solids ac.ivld ha.ivld IQADFX 10 1 - qty/ qty/ Dry or total atmospheric deposition ac.ivld ha.ivld of QUALOFIQADCN 10 1 - qty/ft3 qty/l Concentration of QUALOF in precip for wet atmospheric deposition-------------------------------------------------------------------------------
4.7(2).2 Group ATEMP
Identical to group ATEMP in module PERLND. See Section 4.7(1).2 for documentation.
4.7(2).3 Group SNOW
Identical to group SNOW in module PERLND. See Section 4.7(1).3 for documentation.
Catalog for IMPLND Module
693
Figure 4.7(2)-1 Groups of time series associated with the IMPLND Module
Catalog for IMPLND Module
694
4.7(2).4 Group IWATER ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section IWATER: IMPS 1 1 * in mm Total water stored in the ILS RETS 1 1 * in mm Retention storage SURS 1 1 * in mm Surface (overland flow) storage SURO 1 1 - in/ivld mm/ivld Surface outflow PET 1 1 - in/ivld mm/ivld Potential E-T, adjusted for snow cover and air temperatureIMPEV 1 1 - in/ivld mm/ivld Total simulated E-T SURI 1 1 - in/ivld mm/ivld Surface inflow (including any lateral inflow if RTLIFG=1) Input time series required to compute the above: Group EXTNL PETINP PREC required if snow not considered (CSNOFG= 0) SURLI optional
Group ATEMP AIRTMP only required if section ATEMP is inactive and CSNOFG= 1 Group SNOW only required if section SNOW is RAINF inactive and snow is considered SNOCOV (CSNOFG= 1) WYIELD
-------------------------------------------------------------------------------
Catalog for IMPLND Module
695
4.7(2).5 Group SOLIDS ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section SOLIDS: SLDS 1 1 * tons/ac tonnes/ha Storage of solids on surfaceSOSLD 1 1 - tons/ tonnes/ Washoff of solids from surface ac.ivld ha.ivld Input time series required to compute the above: Group EXTNL always required PREC SLSLD optional Group IWATER only required if section IWATER SURO is inactive SURS-------------------------------------------------------------------------------
Catalog for IMPLND Module
696
4.7(2).6 Group IWTGAS ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section IWTGAS: SOTMP 1 1 * Deg F Deg C Temperature of surface outflow SODOX 1 1 * mg/l mg/l DO concentration in surface outflowSOCO2 1 1 * mg/l mg/l CO2 concentration in surface outflow SOHT 1 1 - BTU/ kcal/ Heat energy in surface outflow ac.ivld ha.ivld (relative to freezing point)SODOXM 1 1 - lb/ kg/ Flux of DO in surface outflow ac.ivld ha.ivldSOCO2M 1 1 - lb/ kg/ Flux of CO2 in surface outflow ac.ivld ha.ivld Input time series required to compute the above: Group ATEMP only required if section ATEMP AIRTMP is inactive Group SNOW only required if section SNOW WYIELD is inactive and snow is considered (CSNOFG= 1) Group IWATER only required if section IWATER SURO is inactive
-------------------------------------------------------------------------------
Catalog for IMPLND Module
697
4.7(2).7 Group IQUAL ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section IQUAL: SQO NQOF 1 * qty/ac qty/ha Storage of QUALOF on the surfaceIQADDR NQ 1 - qty/ qty/ Dry or total atmospheric deposition ac.ivld ha.ivld of QUALIQADWT NQ 1 - qty/ qty/ Wet deposition of QUAL ac.ivld ha.ivldSOQS NQSD 1 - qty/ qty/ Total flux of QUALSD from surface ac.ivld ha.ivldSOQO NQOF 1 - qty/ qty/ Washoff of QUALOF from surface ac.ivld ha.ivldSOQUAL NQ 1 - qty/ qty/ Total outflow of QUAL from the ac.ivld ha.ivld surface SOQOC NQOF 1 - qty/ft3 qty/l Concentration of QUALOF in surface outflow SOQC NQ 1 - qty/ft3 qty/l Concentration of QUAL in surface outflow (QUALSD+QUALOF) Input time series required to compute the above: Group EXTNL IQADFX only required if dry or total atmospheric deposition is being simulated IQADCN only required if wet atmospheric PREC deposition is being simulated
Group IWATER only required if section IWATER is inactive SURO only required if one or more QUALs are QUALOFs, or if SOQC is required for one or more QUALs Group SOLIDS only required if section SOLIDS SOSLD is inactive and one or more QUALs are QUALSDs -------------------------------------------------------------------------------
Catalog for RCHRES Module
698
4.7(3) Catalog for RCHRES module The time series groups associated with this application module are shown in Figure4.7(3)-1. The members contained within each group are documented in the following tables.
Note: exit-specific, output time series are computed (available) only when NEXITSis greater than 1.
4.7(3).1 Group EXTNL
-------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------
Time series external to module RCHRES (input only):
PREC 1 1 - in/ivld mm/ivld Precip on surface of the RCHRES (requires AUX1FG = 1) POTEV 1 1 - in/ivld mm/ivld Potential evaporation from the surface (requires AUX1FG = 1) COLIND NEXITS 1 - none none Time series indicating which (pair of) columns in RCHTAB are used to evaluate f(VOL) component of outflow demand OUTDGT NEXITS 1 - ft3/s m3/s g(t) component of outflow demand if no CATEGORY block is present.COTDGT NEXITS CAT - ft3/s m3/s g(t) component of outflow demand by category if CATEGORY block is presentIVOL 1 1 - ac.ft/ Mm3/ Inflow to the RCHRES if no CATEGORY ivld ivld block is present (Mm3 = 10**6 m3)CIVOL CAT 1 - ac.ft/ Mm3/ Inflow of water belonging to each ivld ivld category if CATEGORY block is presentICON NCONS 1 - qty/ivld qty/ivld Inflow of conservative constituentsSOLRAD 1 1 - Ly/ivld Ly/ivld Solar radiation CLOUD 1 1 - tenths tenths Cloud cover (range 0 - 10) DEWTMP 1 1 - DegF DegC DewpointGATMP 1 1 - DegF DegC Air temperature at met. stationWIND 1 1 - miles/ km/ Wind movement ivld ivld
Catalog for RCHRES Module
699
TGRND 1 1 - DegF DegC Temperature of ground beneath stream bedPHVAL 1 1 - pH (used in Section GQUAL)ROC 1 1 - moles/l moles/l Free radical oxygen concentration (used in Section GQUAL)BIO NGQUAL 1 - mg(bio)/l mg(bio)/l Biomass active in biodegradation (used in Section GQUAL) COADFX NCONS 1 - qty/ qty/ Dry or total atmospheric deposition ac/ivld ha/ivld of conservativeCOADCN NCONS 1 - conc conc Concentration of conservative in rain for wet atmospheric depositionGQADFX NGQUAL 1 - qty/ qty/ Dry or total atmospheric deposition ac/ivld ha/ivld of qualGQADCN NGQUAL 1 - concu/l concu/l Concentration of qual in rain for for wet atmospheric depositionNUADFX 3 1 - lb/ kg/ Dry or total atmospheric deposition ac.ivld ha.ivld of inorganic nutrient. Subscript indicates: NO3, NH3, PO4.NUADCN 3 1 - mg/l mg/l Concentration of nutrient in rain for wet atmospheric depositionPLADFX 3 1 - lb/ kg/ Dry or total atmospheric deposition ac/ivld ha/ivld of organics. Subscript indicates: nitrogen, phosphorus, carbonPLADCN 3 1 - mg/l mg/l Concentration of organic in rain for wet atmospheric deposition. Subscript same as above.-------------------------------------------------------------------------------
Note: CAT = one of the two-character ID tags from the CATEGORY block
Catalog for RCHRES Module
700
Figure 4.7(3)-1 Groups of time series associated with the RCHRES Module
Catalog for RCHRES Module
701
4.7(3).2 Group HYDR
------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section HYDR:
VOL 1 1 * ac.ft Mm3 Volume of water in the RCHRES AUX1FG must be 1 for next 5 members to be computed: DEP 1 1 * ft m Depth at specified location STAGE 1 1 * ft m Stage (DEP+STCOR) AVDEP 1 1 * ft m Average depth (volume/surface area) TWID 1 1 * ft m Mean topwidth (surface area/length) HRAD 1 1 * ft m Hydraulic radiusSAREA 1 1 * ac ha Surface area AUX2FG must be 1 for next 2 members to be computed:AVVEL 1 1 * ft/s m/s Average velocity (RO/VOL) AVSECT 1 1 * ft2 m2 Average cross-sectional area of RCHRES (VOL/length) AUX3FG must be 1 for next 2 members to be computed:USTAR 1 1 * ft/s m/s Shear velocityTAU 1 1 * lb/ft2 kg/m2 Bed shear stressRO 1 1 * ft3/s m3/s Total rate of outflow from RCHRES CRO CAT 1 * ft3/s m3/s Rates of outflow of each categoryO NEXITS 1 * ft3/s m3/s Rates of outflow through individual exits (available only if NEXITS > 1)CO NEXITS CAT * ft3/s m3/s Rates of outflow through individual exits of each categoryCDFVOL NEXITS CAT * ac.ft Mm3 Current cumulative deficit of each category demand by exitPRSUPY 1 1 - ac.ft/ Mm3/ Volume of water contributed by ivld ivld precipitation on surface VOLEV 1 1 - ac.ft/ Mm3/ Volume of water lost by evaporation ivld ivld ROVOL 1 1 - ac.ft/ Mm3/ Total volume of outflow from RCHRES ivld ivld CROVOL CAT 1 - ac.ft/ Mm3/ Total volume of outflow from RCHRES ivld ivld of each categoryOVOL NEXITS 1 - ac.ft/ Mm3/ Volume of outflow through individual ivld ivld exits (available only if NEXITS > 1)COVOL NEXITS CAT - ac.ft/ Mm3/ Volume of outflow through individual ivld ivld exits of each category------------------------------------------------------------------------------- Notes: 1. Mm3 = 10**6 m3 2. Exit-specific time series are computed only if NEXITS > 1 3. CAT = must be one of the two-character tags from the CATEGORY block rather than an integer
Catalog for RCHRES Module
702
Input time series required to compute the above:
Group EXTNL IVOL (also in group INFLOW) optional - not used if CATEGORY block is present CIVOL (also in group INFLOW) optional - used only if CATEGORY block is present PREC optional POTEV optional COLIND required only if ODFVFG is negative for one or more outflow demands OUTDGT required only if ODGTFG is >0 for one or more outflow demands COTDGT required only if ODGTFG is >0 for one or more outflow demands -------------------------------------------------------------------------------
If there are any active categories, then the total inflow to a reach is the sum ofall category inflows. These inflows are input as time series CIVOL, and IVOL iscalculated (by the program) as their sum instead of being input.
Catalog for RCHRES Module
703
4.7(3).3 Group ADCALC ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section ADCALC: None of the computed time series are outputtable; they are passed internally to anyactive "quality" sections of the RCHRES module Input time series required to compute the above:
Group HYDR only required if section HYDR VOL is inactive O-------------------------------------------------------------------------------
Catalog for RCHRES Module
704
4.7(3).4 Group CONS ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series computed by module section CONS: CON NCONS 1 * concid concid Concentration of conservative constituentsCOADDR NCONS 1 - qty/ivld qty/ivld Dry or total atmospheric deposition of conservativeCOADWT NCONS 1 - qty/ivld qty/ivld Wet atmospheric deposition of conservativeROCON NCONS 1 - qty/ivld qty/ivld Total outflow of conservativesOCON NEXITS NCONS - qty/ivld qty/ivld Outflow of conservatives through individual exits; available if NEXITS > 1 Input time series required to compute the above: Group EXTNL COADFX only required if dry or total atmospheric deposition is being simulated COADCN only required if wet atmospheric PREC deposition is being simulated ICON (also in group INFLOW) optional
Group HYDR SAREA only required if atmospheric deposition is being simulated-------------------------------------------------------------------------------
Catalog for RCHRES Module
705
4.7(3).5 Group HTRCH --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr --------------------------------------------------------------------------------
Time series computed by module section HTRCH:
TW 1 1 * DegF DegC Simulated water temperature AIRTMP 1 1 * DegF DegC Air temperature, adjusted for elev. difference between gage and RCHRESHTEXCH 1 1 - BTU/ivld kcal/ivld Net heat exchanged with atmosphere and stream bedROHEAT 1 1 - " " Total outflow of thermal energy through active exitsOHEAT NEXITS 1 - " " Outflow of thermal energy through individual exitsHTCF4 7 1 - BTU/ft2/ kcal/m2/ Components of heat exchange per ivld ivld unit area of surface: 1) total, 2) solar radiation, 3) longwave radiation, 4) evaporation, 5) conduction, 6) percipitation, 7) bed exchange (positive = gain of heat). Input time series required to compute the above: Group INFLOW optional IHEAT optional
Group EXTNL always required SOLRAD PREC optional CLOUD DEWTMP GATMP WIND Group HYDR only required if section HYDR AVDEP is inactive
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Catalog for RCHRES Module
706
4.7(3).6 Group SEDTRN --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------- Time series computed by module section SEDTRN SSED 4 1 * mg/l mg/l Suspended sediment concentrations RSED 10 1 * ton tonne Sediment storages BEDDEP 1 1 * ft m Bed depth (thickness) DEPSCR 4 1 - ton/ivld tonne/ivld Deposition (positive) or scour (negative)ROSED 4 1 - " " Total outflows of sediment from the RCHRES OSED NEXITS 4 - " " Outflows of sediment through individual exits Note: In the above, the subscript with maximum value =4 selects the sedimentfraction - 1 for sand, 2 for silt, 3 for clay, and 4 for the sum of sand silt andclay. The subscript with maximum value =10 selects the following: 1 suspendedsand, 2 suspended silt, 3 suspended clay, 4 bed sand, 5 bed silt 6 bed clay, 7total sand, 8 total silt, 9 total clay, and 10 total of 7,8,9. Input time series required to compute the above: Group INFLOW ISED(*) inflows of sand, silt, and clay to the RCHRES; optional Group HYDR only required if Section HYDR is TAU inactive AVDEP AVVEL RO --- HRAD | only required if SANDFG = 1 or 2 TWID --- Group HTRCH only required if Section HTRCH is TW inactive and SANDFG = 1 or 2
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Catalog for RCHRES Module
707
4.7(3).7 Group GQUAL
--------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------- Time series computed by module section GQUAL: DQAL NGQUAL 1 * concu/l concu/l Dissolved concentration of qual.SQAL 6 NGQUAL * concu/mg concu/mg Concentration of qual on sediment. First subscript selects: 1 susp sand 2 susp silt 3 susp clay 4 bed sand 5 bed silt 6 bed clayRDQAL NGQUAL 1 * qty qty Total storage of qual in dissolved formRSQAL 12 NGQUAL * qty qty Storage of sediment-associated qual. First subscript selects: 1 susp sand 2 susp silt 3 susp clay 4 susp total 5 bed sand 6 bed silt 7 bed clay 8 bed total 9 total on sand 10 total on silt 11 total on clay 12 grand totalRRQAL NGQUAL 1 * qty qty Total storage of qual in the RCHRES PDQAL NGQUAL 1 - qty/ivld qty/ivld Input to this qual in this RCHRES, from decay of parent qualsGQADDR NGQUAL 1 - qty/ivld qty/ivld Dry or total atmospheric deposition of qualGQADWT NGQUAL 1 - qty/ivld qty/ivld Wet atmospheric deposition of qualDDQAL 7 NGQUAL - qty/ivld qty/ivld Decay of dissolved qual. First subscript selects decay path: 1 hydrolysis 2 oxidation 3 photolysis 4 volatilization 5 biodegradation 6 general (other) 7 total of 1-6 .RODQAL NGQUAL 1 - qty/ivld qty/ivld Total outflow of dissolved qual from the RCHRES DSQAL 4 NGQUAL - qty/ivld qty/ivld Deposition/scour of qual. First subscript selects carrier: 1 sand 2 silt 3 clay 4 total ROSQAL 4 NGQUAL - qty/ivld qty/ivld Total outflow of sediment-associated qual from RCHRES. First subscript selects carrier: 1 sand 2 silt 3 clay 4 total SQDEC 7 NGQUAL qty/ivld qty/ivld Decay of sediment-associated qual on: 1 susp sand 2 susp silt 3 susp clay 4 bed sand 5 bed silt 6 bed clay 7 total
Catalog for RCHRES Module
708
ADQAL 7 NGQUAL - qty/ivld qty/ivld Adsorption/desorption between dissolved state and: 1 susp sand 2 susp silt 3 susp clay 4 bed sand 5 bed silt 6 bed clay 7 total ODQAL NEXITS NGQUAL- qty/ivld qty/ivld Outflow of dissolved qual through individual exits. OSQAL NEXITS NGQ3 - qty/ivld qty/ivld Outflows of sediment-associated qual through individual exits. Second subscript selects: 1 sand, first qual 2 silt, first qual 3 clay, first qual (NGQ3= 4 sand, second qual NGQUAL*3) etc.Input time series required to compute the above:
Group INFLOW IDQAL optional ISQAL(*) optional
Group EXTNL GQADFX only required if dry or total atmospheric deposition is being simulated GQADCN only required if wet atmospheric PREC deposition is being simulated
PHVAL if there is hydrolysis, PHFLAG=1, and Section PHCARB is inactive ROC if there is free radical oxidation, and ROXFG=1 BIO(I) if qual number I undergoes biodegradation and GQPM2(7,I)=1 CLOUD if there is photolysis, and CLDFG=1 WIND if there is volatilization and water body is a lake (LKFG=1)
Group HYDR only required if Section HYDR is inactive AVDEP See below AVVEL if volatilization is on and water body is a flowing stream (LKFG=0) SAREA only required if atmospheric deposition is being simulated
Group HTRCH only required if Section HTRCH TW inactive and TEMPFG=1
Group PLANK only required if Section PLANK is inactive or PHYFG=0
Catalog for RCHRES Module
709
PHYTO if there is photolysis and PHYTFG=1
Group SEDTRN only required if Section SEDTRN is inactive SSED(4) if there is photolysis and SDFG=1 --------------------------------------------------------------------------------
Note: AVDEP is required if Section HYDR is inactive and: 1. There is photolysis or 2. There is volatilization and a. The water body is a lake or b. The water body is a free-flowing stream and REAMFG>1
Catalog for RCHRES Module
710
4.7(3).8.1 Group OXRX --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------- Time series computed by module section OXRX: DOX 1 1 * mg/l mg/l DO concentrationBOD 1 1 * mg/l mg/l BOD concentration SATDO 1 1 * mg/l mg/l Saturation DO concentration OXCF1 2 1 - lb/ivld kg/ivld Total outflows of DO (OXCF1(1,1)) and BOD (OXCF1(2,1)) from the RCHRES OXCF2 NEXITS 2 - lb/ivld kg/ivld Outflows of DO and BOD through individual exitsIn the above, the first subscript selects the exit. The second selects the constituent: 1 means DO, 2 means BOD. Input time series required to compute the above: Group INFLOW IDOX optional IBOD optional Group EXTNL WIND only needed if LKFG=1 (lake) Group HYDR only required if section HYDR AVDEP is inactive AVDEP Group HTRCH only required if section HTRCH TW is inactive
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Catalog for RCHRES Module
711
4.7(3).8.2 Group NUTRX --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------- Time series computed by module section NUTRX: DNUST 6 1 * mg/l mg/l Dissolved nutrient concentrations; Subscript 1: 1=NO3, 2=TAM, 3=NO2, 4=PO4, 5=NH4+, 6=NH3.SNH4 3 1 * mg/kg mg/kg Particulate NH4-N concentrations; Subscript 1: 1=sand, 2=silt, 3=claySPO4 3 1 * mg/kg mg/kg Particulate PO4-P concentrations; Subscript 1: 1=sand, 2=silt, 3=clayDNUST2 6 1 * lbs kg Dissolved nutrient storages; same subscript values as DNUSTRSNH4 12 1 * lbs kg Particulate NH4-N storages; 1-3= suspended sand, silt, clay, 4=total suspended, 5-7=bed sand, silt, clay, 8=total bed, 9-11=total sand, total silt, total clay, 12=grand totalRSPO4 12 1 * lbs kg Particulate PO4-P storages; same subscript values as RSNH4NUST 4 1 * lbs kg Total nutrient storages in RCHRES, (dissolved + particulate); Subscript 1: 1=NO3,2=TAM,3=NO2,4=PO4NUADDR 3 1 - lb/ivld kg/ivld Dry or total atmospheric deposition of nutrient; Subscript 1: 1=NO3, 2=TAM, 3=PO4NUADWT 3 1 - lb/ivld kg/ivld Wet atmospheric deposition of nutrient; Subscript same as NUADDRNUCF1 4 1 - lb/ivld kg/ivld Total outflow of dissolved nutrient; Same subscript values as NUSTNUCF2 4 2 - lb/ivld kg/ivld Total outflow of particulate NH4 and PO4; Subscript 1: 1=(on) sand, 2=silt, 3=clay, 4=total; Subscript 2: 1 = NH4, 2 = PO4NUCF3 4 2 - lb/ivld kg/ivld Scour/deposition fluxes of particulate NH4 and PO4; + = scour, - = deposition; same subscript values as NUCF2
Catalog for RCHRES Module
712
NUCF4 6 1 - lb/ivld kg/ivld Process fluxes for NO3; Subscript 1: 1=nitrification, 2=denitrification, 3=BOD decay, 4=phytoplankton growth/respir., 5=zooplankton death/respir., 6=benthic algae growth/respir.NUCF5 7 1 - lb/ivld kg/ivld Process fluxes for TAM; Subscript 1: 1=nitrification, 2=volatilization, 3=benthal release, 4=BOD decay, 5=phytoplankton growth/respir., 6=zooplankton death/respir., 7=benthic algae growth/respir.NUCF6 1 1 - lb/ivld kg/ivld Nitrification flux for NO2; (net gain (+) or loss (-) of NO2)NUCF7 5 1 - lb/ivld kg/ivld Process fluxes for PO4; Subscript 1: 1=benthal release, 2=BOD decay, 3=phytoplankton growth/respir., 4=zooplankton death/respir., 5=benthic algae growth/respir.NUCF8 4 2 - lb/ivld kg/ivld Adsorption (+) or desorption (-) of NH4 and PO4; Subscript 1: 1=sand, 2=silt, 3=clay; Subscript 2: 1=NH4, 2=PO4NUCF9 5 4 - lb/ivld kg/ivld Outflow of dissolved nutrients through individual exits; Subscript 1 selects exit, Subscript 2: same as NUCF1OSNH4 5 3 - lb/ivld kg/ivld Outflows of particulate NH4; Subscript 1 selects exit, Subscript 2: 1=sand, 2=silt, 3=clayOSPO4 5 3 - lb/ivld kg/ivld Outflows of particulate PO4-P; Subscript values same as OSNH4 Input time series required to compute the above: Group INFLOW INO3, ITAM, INO2, all optional IPO4, ISNH4,ISPO4
Group EXTNL NUADFX only required if dry or total atmospheric deposition is being simulated NUADCN only required if wet atmospheric PREC deposition is being simulated
Group HYDR SAREA only required if atmospheric deposition is being simulated
Catalog for RCHRES Module
713
Group HTRCH only required if section HTRCH TW is inactive
Group SEDTRN only required if Section SEDTRN is RSED, SSED, OSED, inactive and if particulate NH4 or ROSED, DEPSCR PO4 is simulated NOTE: Ammonia, nitrite and ortho-phosphate may, or may not, be simulated, dependingon the values the user assigns to TAMFG, NO2FG and PO4FG. If a constituent is notsimulated, those time series associated with it in this list should be ignored. --------------------------------------------------------------------------------
Catalog for RCHRES Module
714
4.7(3).8.3 Group PLANK
--------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr --------------------------------------------------------------------------------
Time series computed by module section PLANK: PKST3 7 1 * mg/l mg/l A group of state variablesIn the above, the first subscript selects the state variable: 1 for dead refractory organic N (ORN)2 for dead refractory organic P (ORP)3 for dead refractory organic C (ORC)4 for total organic N (TORN) 5 for total organic P (TORP) 6 for total organic C (TORC) 7 for potential BOD (POTBOD) PHYTO 1 1 * mg/l mg/l Phytoplankton concentration ZOO 1 1 * organism/l organism/l Zooplankton populationBENAL 1 1 * mg/m2 mg/m2 Benthic algae PHYCLA 1 1 * ug/l ug/l Phytoplankton as chlorophyll aBALCLA 1 1 * ug/m2 ug/m2 Benthic algae as chlorophyll a
PLADDR 3 1 - lb/ivld kg/ivld Dry or total atmospheric deposition of organicsPLADWT 3 1 - lb/ivld kg/ivld Wet atmospheric deposition of organicsIn the above, the first subscript selects the constituent:1 for ORN, 2 for ORP, 3 for ORC
PKCF1 5 1 - lb/ivld kg/ivld Total outflows from the RCHRESIn the above, the first subscript selects the constituent:1 for phytoplankton, 2 for zooplankton, 3 for ORN, 4 for ORP, 5 for ORC PKCF2 NEXITS 5 - lb/ivld kg/ivld Outflows through individual exits In the above, the first subscript selects the exit, the second selects theconstituent -- same code as for PKCF1.
Input time series required to compute the above:
Group INFLOW IPHYTO, IZOO, IORN, all are optional IORP, IORC
Catalog for RCHRES Module
715
Group EXTNL SOLRAD required PLADFX only required if dry or total atmospheric deposition is being simulated PLADCN only required if wet atmospheric PREC deposition is being simulated Group HYDR SAREA only required if atmospheric deposition is being simulated
Group HTRCH only required if section HTRCH TW is inactive Group SEDTRN only required if section SEDTRN SSED(2) is inactive SSED(3)
NOTE: Phytoplankton, zooplankton and benthic algae may, or may not, be simulated,depending on the values the user assigns to PHYFG, ZOOFG and BALFG. If aconstituent is not simulated, those time series associated with it in this listshould be ignored. --------------------------------------------------------------------------------
Catalog for RCHRES Module
716
4.7(3).8.4 Group PHCARB --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr -------------------------------------------------------------------------------- Time series computed by module section PHCARB: PHST 3 1 * see below State variables In the above, the first subscript selects the state variable: 1 for total inorganic carbon (TIC) -- units mg/l2 for carbon dioxide (CO2) -- units mg/l3 for pH PHCF1 2 1 - lb/ivld kg/ivld Total outflows of TIC and CO2 In the above, the first subscript selects the constituent:1 for TIC, 2 for CO2 PHCF2 NEXITS 2 - lb/ivld kg/ivld Outflows of TIC and CO2 through individual exitsIn the above, the first subscript selects the exit and the second the constituent -- same code as for PHCF1 Input time series required to compute the above:
Group INFLOW ITIC optional ICO2 optional Group CONS only required if section CONS is inactive CON(ALKCON) concentration units must be mg/l as CaCO3 Group HTRCH only required if section HTRCH TW is inactive
--------------------------------------------------------------------------------
Catalog for RCHRES Module
717
4.7(3).10 Groups INFLOW, ROFLOW and OFLOW The members in these groups represent the total inflow, total outflow and outflowthrough individual RCHRES exits of every simulated constituent. These groups wereincluded in the catalog to make it easier for users to specify the linkagesrepresenting time series passed from one RCHRES to another. For example, assumethe RCHRES's in a run have sections HYDR, HTRCH and OXRX active, and the NETWORKBlock contains: ********************************************************************************NETWORK <-Volume-> <-Grp> <-Member-><--Mult-->Tran <-Target vols> <-Grp> <-Member-> ***<Name> # <Name> # #<-factor->strg <Name> # # <Name> # # *** RCHRES 1 ROFLOW RCHRES 2 INFLOWRCHRES 2 OFLOW 2 RCHRES 3 INFLOW********************************************************************************
These entries mean that the entire outflow from RCHRES 1 goes to RCHRES 2, and thatthe outflow through exit 2 of RCHRES 2 goes to RCHRES 3. Because the "member name"fields have been left blank, HSPF will automatically expand the above entries,generating an entry for each member which is active in this run. In this case,there will be 4 generated entries because 4 constituents are being simulated(water, heat, DO and BOD). The second set of generated entries would be: ********************************************************************************NETWORK <-Volume-> <-Grp> <-Member-><--Mult-->Tran <-Target vols> <-Grp> <-Member-> ***<Name> # <Name> # #<-factor->strg <Name> # # <Name> # # *** RCHRES 2 OFLOW OVOL 2 1 1.0 RCHRES 3 INFLOW IVOL 1 1 RCHRES 2 OFLOW OHEAT 2 1 1.0 RCHRES 3 INFLOW IHEAT 1 1 RCHRES 2 OFLOW OXCF2 2 1 1.0 RCHRES 3 INFLOW OXIF 1 1 RCHRES 2 OFLOW OXCF2 2 2 1.0 RCHRES 3 INFLOW OXIF 2 1 ********************************************************************************
Thus, the user can specify the linkage between two RCHRES's with a single entry,instead of having to supply an entry for every constituent passed between them.
Catalog for RCHRES Module
718
4.7(3).10.1 GROUP INFLOW The members in this group represent the inflows to a RCHRES. Note that each memberlisted below is "available" for use only if the module section to which it belongsis active. --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Module ConstituentName values n section 1 2 d Engl Metr --------------------------------------------------------------------------------IVOL 1 1 - ac.ft/ Mm3/ HYDR Water ivld ivld (Note: Mm3=10**6 m3) CIVOL CAT 1 - ac.ft/ Mm3/ HYDR Water, by Category ivld ivld (Note: CAT=Category tag)
ICON NCONS 1 - qty/ qty/ CONS Conservatives ivld ivld IHEAT 1 1 - BTU/ kcal/ HTRCH Heat (relative to ivld ivld freezing) ISED 3 1 - ton/ tonne/ SEDTRN Sand, silt, and clay ivld ivld IDQAL NGQUAL 1 - qty/ qty/ GQUAL Dissolved general ivld ivld quality constituents ISQAL 3 NGQUAL - qty/ qty/ GQUAL General quality const- ivld ivld ituent associated with: 1 Sand, 2 Silt, 3 Clay
OXIF 2 1 - lb/ivld kg/ivld OXRX 1=DO, 2=BOD
NUIF1 4 1 - lb/ kg/ NUTRX 1=NO3, 2=TAM, 3=NO2, ivld ivld 4=PO4
NUIF2 3 2 - lb/ kg/ NUTRX 1=particulate NH4, ivld ivld 2=particulate PO4 on 1=sand, 2=silt, 3=clay
PKIF 5 1 - lb/ kg/ PLANK 1=Phyto, 2=Zoo, 3=ORN, ivld ivld 4=ORP, 5=ORC
PHIF 2 1 - lb/ivld kg/ivld PHCARB 1=TIC, 2=CO2 --------------------------------------------------------------------------------
Catalog for RCHRES Module
719
4.7(3).10.2 Group ROFLOW The members in this group represent the total outflow from a RCHRES. Note that amember is "available" for use only if the module section to which it belongs isactive. --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Module Constituent values n section 1 2 d Engl Metr -------------------------------------------------------------------------------- ROVOL 1 1 Water
CROVOL CAT 1 Water, by category (CAT=Category ID tag)
ROCON NCONS 1 Conservatives ROHEAT 1 1 Heat ROSED 3 1 Sand, silt, and clay See data forRODQAL NGQUAL 1 corresponding Dissolved general qual. member in group ROSQAL 3 NGQUAL INFLOW Sediment-associated qual. OXCF1 2 1 DO, BOD NUCF1 4 1 NO3, TAM, NO2, PO4
NUCF2 3 2 Particulate NH4 and PO4 (sand, silt, clay)
PKCF1 5 1 Phyto, Zoo, ORN, ORP, ORC PHCF1 2 1 TIC, CO2
--------------------------------------------------------------------------------
Catalog for RCHRES Module
720
4.7(3).10.3 Group OFLOW The members in this group represent the outflows through the individual exits ofa RCHRES. Note that a member is available for use only if the module section towhich it belongs is active. Also, these time series are available for use only ifthe number of exit gates (NEXITS) is greater than 1.
For each member, the RCHRES exit is selected by the value given to the firstsubscript. --------------------------------------------------------------------------------<---- Member ----> K Units Max subscr i (external) Module ConstituentName values n section 1 2 d Engl Metr --------------------------------------------------------------------------------OVOL NEXITS 1 Water
CROVOL NEXITS CAT Water, by category (CAT=Category ID tag)
OCON NEXITS NCONS Conservatives OHEAT NEXITS 1 Heat OSED NEXITS 3 Sand, silt, and clay ODQAL NEXITS NGQUAL See data for Dissolved general qual. corresponding OSQAL NEXITS NGQ3 member in group Sediment-associated qual. INFLOW OXCF2 NEXITS 2 DO, BOD NUCF9 NEXITS 4 NO3, NH3, NO2, PO4 OSNH4 NEXITS 3 Particulate NH4 (sand, silt, clay)
OSPO4 NEXITS 3 Particulate PO4 (sand, silt, clay)
PKCF2 NEXITS 5 Phyto, Zoo, ORN, ORP, ORC PHCF2 NEXITS 2 TIC, CO2 --------------------------------------------------------------------------------
Catalog for COPY Module
721
4.7(11) Catalog for COPY module The members contained within each group are documented in the tables which follow. 4.7(11).1 Group INPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series input to module COPY: POINT NPT 1 * anything Point-valued input time seriesMEAN NMN 1 - anything Mean-valued input time series ------------------------------------------------------------------------------- 4.7(11).2 Group OUTPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series output by module COPY: POINT NPT 1 * anything Point-valued output time series MEAN NMN 1 - anything Mean-valued output time series Input time series required to produce the above: Group INPUT POINT required if NPT> 0 MEAN required if NMN> 0 -------------------------------------------------------------------------------
Catalog for PLTGEN Module
722
4.7(12) Catalog for PLTGEN module There is only one time series group associated with this module; group INPUT, whichcontains all point-valued and/or mean-valued members that are to be plotted. Thismodule does not have an output group because all its output goes to the "plotfile", which is documented in Section 4.4(12) of Part E. 4.7(12).1 Group INPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series input to module PLTGEN: POINT NPT 1 * anything Point-valued input time seriesMEAN NMN 1 - anything Mean-valued input time series -------------------------------------------------------------------------------
Catalog for DISPLY Module
723
4.7(13) Catalog for DISPLY module There is only one time series group (INPUT) with one member (TIMSER) associatedwith this module since the module displays only one time series at a time. Thismodule does not have an output group because all its output goes to the "displayfile" (printed). 4.7(13).1 Group INPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series input to module DISPLY: TIMSER 1 1 - anything A mean-valued input time series -------------------------------------------------------------------------------
Catalog for DURANL Module
724
4.7(14) Catalog for DURANL module There is only one time series group (INPUT) with one member (TIMSER) associatedwith this module since the module analyzes only one time series at a time. Thismodule does not have an output group because all its output is printed. The formatis documented in Section 4.2(14) of Part E. 4.7(14).1 Group INPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series input to module DURANL: TIMSER 1 1 - anything A mean-valued input time series -------------------------------------------------------------------------------
Catalog for GENER Module
725
4.7(15) Catalog for GENER module This module has both input and output groups, like module COPY. The members contained within each group are documented in the tables which follow. 4.7(15).1 Group INPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series input to module GENER: ONE 1 1 - anything First input time series TWO 1 1 - anything Second input time series-------------------------------------------------------------------------------
4.7(15).2 Group OUTPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series output by module GENER: TIMSER 1 1 - anything Output time series (mean-valued) Input time series required to produce the above: Group INPUT ONE Always required, unless OPCODE=24. TWO Only required if generation option needs two inputs. -------------------------------------------------------------------------------
Catalog for MUTSIN Module
726
4.7(16) Catalog for MUTSIN module The members contained within each group are documented in the tables which follow. 4.7(16).1 Group OUTPUT ------------------------------------------------------------------------------- <---- Member ----> K Units Max subscr i (external) Description/comment Name values n 1 2 d Engl Metr ------------------------------------------------------------------------------- Time series output by module MUTSIN POINT NPT 1 * anything Point-valued output time series MEAN NMN 1 - anything Mean-valued output time series --------------------------------------------------------------------------------
FORMATS Block
727
4.8 FORMATS Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout ******
FORMATS *** <ft><------------------------- obj-fmt ---------------------------------------->. *** line immed above repeats until all formats have been covered . .END FORMATS ******************************************************************************** Details -------------------------------------------------------------------------------- Symbol FORTRAN Format Comment Name(s) -------------------------------------------------------------------------------- <ft> FMTCOD I4 Identifying number which corresponds to format number in EXT SOURCES or TARGETS Blocks. <obj-fmt> FORM(19) 19A4 Standard FORTRAN object-time format. --------------------------------------------------------------------------------
Explanation
This block is only required if a user wishes to override the default format forreading data on a sequential file (see Section 4.9).
Sequential File Formats
728
4.9 Sequential and PLTGEN/MUTSIN File Formats
Two types of ASCII file formats are available for transfer of data into/out ofHSPF. "Sequential" files allow transfer into HSPF, and PLTGEN/MUTSIN files allowtransfer into and out of HSPF. These file formats are documented below:
4.9.1 Format class HYDFIV - Sequential It is used for the input of 5-minute data. The sequence of information is:
1. Alpha-numeric station number or identifier (this field is not read) 2. Last two digits of calendar year 3. Month 4. Day 5. Card number 1 is for midnight to 3 am. 2 is for 3 am to 6 am. 3 is for 6 am to 9 am. 4 is for 9 am to noon. 5 is for noon to 3 pm. 6 is for 3 pm to 6 pm. 7 is for 6 pm to 9 pm. 8 is for 9 pm to midnight. 6. 36 fields for 5-minute data. The default format is: (1X,3I2,I1,36F2.0) 4.9.2 Format class HYDFIF - Sequential It is used for the input of 15-minute data. The sequence of information is: 1. Alpha-numeric station number or identifier (this field is not read). 2. Last two digits of the calendar year 3. Month 4. Day 5. Card number (same as for HYDFIV above) 6. 12 fields for 15-minute data The default format is: (1X,3I2,I1,12F6.0)
Sequential File Formats
729
4.9.3 Format class HYDHR - Sequential It is used for input of hourly observations. The sequence of information is: 1. Alpha-numeric station number or identifier. (This field is not read) 2. Last two digits of calendar year 3. Month 4. Day 5. Card no: 1 is for a.m. hours 2 is for p.m. hours 6. Twelve fields for hourly data The default format is: (10X,I2,1X,I2,1X,I2,1X,I1,12F5.0) 4.9.4 Format class HYDDAY - Sequential It is used for input of daily observations. The sequence of information is: 1. Alpha-numeric station number or identifier. (This field is not read) 2. Last two digits of calendar year 3. Month 4. Card no: 1 is for days 1-10 2 is for days 11-20 3 is for days 21- 5. Ten fields, for the daily data (11 fields for card number 3) The default format is: (7X,2I2,I1,11F6.0) 4.9.5 Format class HYDSMN - Sequential It is used for input of semi-monthly observations. The sequence of information is: 1. Alpha-numeric station number or identifier. (This field is not read) 2. Last two digits of calendar year 3. Card no: 1 for January through June 2 for July through December 4. Twelve semi-monthly fields The default format is: (7X,I2,I1,12F5.0) Semi-monthly values are distributed to daily values with a transformation functionof SAME.
Sequential File Formats
730
4.9.6 Format class HYDMON - Sequential It is used for input of monthly observations. The sequence of information is: 1. Alpha-numeric station number or identifier. (This field is not read) 2. Last two digits of calendar year 3. Twelve monthly fields The default format is: (6X,I2,12F6.0) Monthly values are distributed to daily values with a transformation function ofSAME. Note that the user can override the above default formats with his own format,supplied in the FORMATS BLOCK. However, the sequence of information within eachrecord cannot be altered.
4.9.7 PLTGEN/MUTSIN File Format
Time series data can be transferred to or from ASCII files having the PLTGEN/MUTSINformat, i.e., the format of files created by the PLTGEN module and readable by theMUTSIN module. This file contains a header, which is 25 lines for PLTGEN and atleast one line for MUTSIN. Each line of data contains a date-time and between oneand ten data values (curves). The sequence of information for each data line isas follows:
1. Identifier (four characters)2. Year 3. Month 4. Day 5. Hour 6. Minute7. Value for curve 1, for this date/time 8. Value for curve 2, for this date/time etc (repeats until data for all curves are supplied)
Format: A4,1X,I5,4I3,10(2X,G12.5)
SPEC-ACTIONS Block
731
4.10 SPEC-ACTIONS Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******
SPEC-ACTIONS Action line: <addrss>------- ----<uvqn> dc ds d t or or tc ts num <oper><f><-l><>< ><yr><m><d><h><m><><> <vari><1><2><3><a><-value--> <> < >< >
Distribute line: ds ct tc ts <kwrd>< > < > <> < > <dff> <frc><frc><frc><frc><frc><frc><frc><frc><frc><frc>
User-defined/multiple variable line: cnt act act <kwrd> <unam>< > <vari><1><2><3> <frc> < > <vari><1><2><3> <frc> < > or <addrss>-------
User-defined variable quantity line: lc ls ac as agfn <kwrd> <uqnm> <oper> <#> <vari><1><2><3><t><multfact> <>< > <>< > < > or <addrss>-------
Condition line (free format):
IF ( ( <quan> <comp> <quan> ) <logop> ( <quan> <comp> <quan> ) ) THEN ... ELSE IF ( <quan> <comp> <quan> ) THEN ... ELSE ... END IF
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (repeats until all special actions have been specified) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .END SPEC-ACTIONS
SPEC-ACTIONS Block
732
Example*******
SPEC-ACTIONS*** Distributions*** kwd ds ct tc ts dff f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 <****>< > < > <> < > <---> <---><---><---><---><---><---><---><---><---><---> DISTRB 1 3 DY 7 ACCUM 0.25 0.5 0.25
*** User-Defined Target Variable Names*** addr addr*** <------> <------>*** kwd varnam ct vari s1 s2 s3 frac oper vari s1 s2 s3 frac oper <****> <----><-> <----><-><-><-> <---> <--> <----><-><-><-> <---> <--> UVNAME MANURE 3 SAMSU 0.7 QUAN SNO3 0.2 QUAN SORGN 0.3 QUAN
*** User-Defined Variable Quantity Lines*** addr*** <------>*** kwd varnam optyp opn vari s1 s2 s3 tp multiply lc ls ac as agfn *** <****> <----> <----> <-> <----><-><-><-><-><--------> <><-> <><-> <--> *** UVQUAN puncom RCHRES 2 CVOL tx 4
*** Action Lines*** addr uvquan*** <------> <---->***optyp range dc ds yr mo da hr mn d t vari s1 s2 s3 ac value tc ts num <****><-><--><>< ><--><-><-><-><-><><> <----><-><-><-><-><--------> <> < >< > PERLND 1 DY 11991/03/15 16:00 1 3 MANURE += 10.0 YR 1 5IF (puncom > 10000) THEN RCHRES 2 4 CVOL pw += puncomEND IFEND SPEC-ACTIONS********************************************************************************
Explanation
In the SPEC-ACTIONS block, the user can change the values of program variables atspecified dates and times. This permits one to model such things as: 1) humanintervention, i.e., plowing or application of fertilizer and pesticide; 2) changesto parameters in ways not possible with the standard inputs; and 3) conditionalactions, i.e., those that are dependent on the value of another program variable.
Special Actions can be performed on variables in the PERLND, IMPLND, RCHRES, COPY,PLTGEN, and GENER modules. The user's input is contained in the SPEC-ACTIONS blockof the UCI file. It is specified in the five different types of lines shown aboveand described fully in the following sections. Output is printed in the Run Inter-preter Output (echo) file, and consists of two types: 1) a listing of all SpecialActions as interpreted by the program, and 2) a summary of each Special Action(value of affected variable before and after the action) as it is implementedduring the run.
SPEC-ACTIONS Block
733
Details of Action line (including REPEAT function)
--------------------------------------------------------------------------------Symbol Fortran Format Comment name(s)--------------------------------------------------------------------------------<oper> OPTYP A6 operation type - valid values are PERLND, IMPLND, RCHRES, or PLTGEN<f> TOPFST I3 first operation to act upon<-l> TOPLST I4 last operation to act upon, 0 or blank means use first operation onlydc CTCODE(1) A2 code specifying time units of deferral of action when an applicable logic condition fails - (MI,HR,DY,MO,YR)ds TSTEP(1) I3 number of CTCODE(1) intervals to defer the action<yr> DATIM(1) I4 year (see starting date field in GLOBAL block for more information) if the date is left blank, then the action is performed every interval of the run<m> DATIM(2) 1X,I2 month<d> DATIM(3) 1X,I2 day<h> DATIM(4) 1X,I2 hour<m> DATIM(5) 1X,I2 minuted DSIND I2 ID number of "DISTRB" line, blank if nonet TYPCOD I2 2-INTEGER, 3-REAL, 4-DOUBLE PRECISION<vari> VNAME A6 variable to act upon, left-justified<1><2><3> CSUB(1-3) 3A3 subscripts for VNAME, blank if none may be 2-character CATEGORY tag if applicable must be integer otherwise<addrss> ADDR I8 memory location (in the OSV) of variable (optional method to specify variable)<a> ACTCOD A3 action code: id number (#) or character (ch). T= target variable, A= action value: # ch effect # ch effect 1 = T= A 2 += T= T+ A 3 -= T= T- A 4 *= T= T*A 5 /= T= T/A 6 MIN T= Min(T,A) 7 MAX T= Max(T,A) 8 ABS T= Abs(A) 9 INT T= Int(A) 10 ^= T= T^A 11 LN T= Ln(A) 12 LOG T= Log10(A) 13 MOD T= Mod(T,A)<value> RVAL or F10.0 "value" of the action to be taken - IVAL I10 see notes below<uvqn> UVQNAM A6 name of User-defined variable quantity containing the "value" of the actiontc CTCODE(2) A2 code specifying time units of "repeat" action - (MI,HR,DY,MO,YR)ts TSTEP(2) I3 number of CTCODE(2) intervals to skip before repeating the actionnum NUMINC I3 number of times to repeat action-------------------------------------------------------------------------------
SPEC-ACTIONS Block
734
Details of Distribution line
----------------------------------------------------------- ---------------------Symbol Fortran Format Comment name(s)--------------------------------------------------------------------------------<kwrd> - A6 keyword (DISTRB) - specifies current line as a "Distribution" lineds DSIND I3 index number - corresponds to the value specified on the standard linect CNT I3 number of separate actions or applications to divide the total application intotc CTCODE A2 code specifying time units of the interval between separate applications or actions - (valid values: MI,HR,DY,MO,YR)ts TSTEP I3 number of CTCODE intervals between separate applications (see CTCODE below)<dff> CDEFFG A5 deferral flag - indicates how to treat deferral of the action(s) under a conditional situation - (valid values: SKIP, SHIFT, ACCUM; default = SKIP)<frc> FRACT(CNT) 10F5 fractions for each of the separate applications--------------------------------------------------------------------------------
Details of User-defined Variable Name line
--------------------------------------------------------------------------------Symbol Fortran Format Comment name(s)--------------------------------------------------------------------------------<kwrd> - A6 keyword (UVNAME) - specifies current line as a "User-defined variable name" line<unam> UNAME A6 user-defined variable namecnt CNT I3 number of actual variables included in aggregate group
Following inputs repeat CNT times (continuation lines if CNT>2)<vari> VNAME A6 actual variable name<1><2><3> CSUB(1-3) 3A3 subscripts for VNAME, blank if none may be 2-character CATEGORY tag if applicable must be integer otherwise<addrss> ADDR I4 address of actual variable
<frc> FRAC F5.2 fraction of total application allocated to each of the actual variablesact ACTCD A4 action code - QUAN, MOVT, MOV1, MOV2 (see notes on UVNAME action code options)--------------------------------------------------------------------------------
SPEC-ACTIONS Block
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Details of User-defined Variable Quantity line
----------------------------------------------------------- ---------------------Symbol Fortran Format Comment name(s)--------------------------------------------------------------------------------<kwrd> - A6 keyword (UVQUAN) - specifies current line as a "User-defined variable quantity" line
<uqnm> UVQNAM A6 user-defined variable quantity name
<oper> OPTYP A6 operation type of base variable
<#> OPTNO I3 operation type number of variable
<vari> VNAME A6 actual variable name of variable
<1><2><3> CSUB(1-3) 3A3 subscripts for VNAME, blank if none may be 2-character CATEGORY tag if applicable must be integer otherwise
<addrss> ADDR I4 address of actual variable
<t> TYPCOD I2 2-INTEGER, 3-REAL, 4-DOUBLE PRECISION
<multfact> UVQMUL F10.0 multiplier to apply to base variable
lc CTCODE(1) A2 code specifying time units of the period to lag base variable (valid values: MI,HR,DY,MO,YR)ls TSTEP(1) I3 number of CTCODE intervals to lag base variableac CTCODE(2) A2 code specifying time units of the period to aggregate base variable (valid values: MI,HR,DY,MO,YR)as TSTEP(1) I3 number of CTCODE intervals to aggregate base variableagfn CTRAN A4 transformation function to use for aggregation of base variable (valid values: SUM, AVER, MAX, MIN.)--------------------------------------------------------------------------------
SPEC-ACTIONS Block
736
Details of Free-Format Conditional lines
----------------------------------------------------------- ---------------------Symbol Fortran Format Comment name(s)--------------------------------------------------------------------------------IF - - Keyword specifying the beginning of a logical condition<quan> CITEM free(10) May be either a UVQUAN name (format A6) or a numeric value (format up to F10.0)<comp> CCODE free(2) Numerical comparison operator. Valid values are: = equal /= not equal > greater than >= greater than or equal > less than >= less than or equal<logop> CLOGOP free(3) Logical operator: AND or ORTHEN - - Keyword specifying the end of a logical conditionELSE - - Keyword specifying that following special actions are an alternative to previous IFs and ELSE IFsELSE IF - - Keyword specifying that following special actions are an alternative to previous IFs and ELSE IFs, provided that the additional condition is also satisfied. Exactly one space must occur between the two words.END IF - - Keyword specifying the end of a logical block. Exactly one space must occur between the two words.--------------------------------------------------------------------------------
*** free(N) denotes that the field may be any length up to N characters, and mayappear in any column, subject to a maximum line length of 80 characters.
SPEC-ACTIONS Block
737
Notes:
The <value> field contains quantitative data for the action to be taken. If thevariable or array element to be acted on is an integer (TYPCOD=2) <value> is readas an integer (IVAL); If it is REAL or DOUBLE PRECISION (TYPCOD=3 or 4), <value>is read as a real number (RVAL). Note that the value must be given in the unitsused internally for the quantity concerned, because no conversion is performed whenit is read in. You can find the internal units by looking up the quantity in theOperations Status Vector (for the module concerned), contained in the Programmer'sSupplement. For example:
1. Pesticide storage (module PERLND) has units of lb/ac (English) and kg/ha(Metric); the same units are used internally and externally.
2. Sediment storage (module PERLND) has internal units of tons/acre (in bothEnglish and Metric systems) but the external units (English and Metric) aretons/acre and tonnes/ha respectively.
Repeat definitionThis feature allows a single Special Action to be repeated at regular intervals.The input that defines the repetition is contained entirely on the standard actionline. The date-time specified on the line is the starting date-time. Therepetition is specified by: 1) CTCODE(2), which defines the time units of theinterval between repetitions, 2) TSTEP(2), which defines the number of CTCODE(2)time steps between repetitions, and 3) NUMINC, which is the number of times toperform the action (for example: if NUMINC is 3, the action will be performed threetimes, i.e., on the specified date-time and two repetitions).
Distribute definitionThis option allows a single Special Action to be split into multiple actions. Theprimary purpose is to distribute a chemical application over time so that it is notall applied to the land segment at once. The additional information needed todefine the distribution is specified on an "associated" line in the Special Actionsblock. An ID number (DSIND) is included on the standard Special Actions line whichpoints to the associated line. This line contains: 1) the keyword "DISTRB", whichidentifies the line as a "distribution" definition line, 2) DSIND, the ID numbercorresponding to the value on the standard line, 3) CNT, the number of separateapplications to divide the total application into, 4) CTCODE and TSTEP, whichdefine the time interval between applications (see discussion of REPEAT definitionabove), and 5) FRACT(CNT), the fraction of the total application represented byeach of the separate applications. Note, the total application is given by <value>(or RVAL), which is specified on the standard Special Action line.
User-defined variable (UVNAME)This option allows the user to define a single name (UVNAME) for one or morestandard variables to be used as the target of a Special Action. If a UVNAME isapplied to multiple standard variables, then any action line referring to thatUVNAME as a target will cause multiple Special Actions to occur. This linecontains: 1) a user defined name (UNAME), limited to six characters; 2) the number(count) of standard variables that are included in the set; 3) the variable names;4) the fractions of the total Special Action quantity that will be applied to eachvariable; and 5) and an optional action code. (See below.)
SPEC-ACTIONS Block
738
UVNAME action code options
QUAN Specify multiple Special Action variables in one line. Each quantityspecified in the UVNAME line is multiplied by the quantity in thecorresponding standard line to generate the final quantity applied to eachof the variables specified in the UVNAME line. This option is designedprimarily to allow a total chemical amount to be applied to multiple soillayers with a single line. This is the default.
MOVT Redistribute current total quantity contained in multiple variables usingpredetermined factors. Each quantity specified in the UVNAME line ismultiplied by the total quantity obtained by summing the current values ofthe individual variables specified in the UVNAME line. This option isdesigned to simulate a plowing operation that completely mixes all materialin two or more zones. This would be accomplished by using quantities thatare the fractions of soil or depth in the individual layers. (This optiondoes not use the "quantity" or Action Code specified in standard line.)
MOV1, Redistribute two quantities in following manner: Variable No. 1 is computedMOV2 by multiplying current value of variable No. 2 by quantity associated with
Variable 1 in the UVNAME line. Variable No. 2 is computed by multiplyingcurrent value of Variable No. 2 by quantity associated with Variable No.2 in the UVNAME line plus the current value of Variable No. 1. This optionis designed to simulate a plowing operation that transfers the material inthe surface zone to the upper zone, and results in the new surface zonehaving the original concentration of the upper zone. This would beaccomplished by using the following two quantities: 1) ratio of amount ofsoil (or depth) in surface layer to amount in upper layer, and 2) subtractsurface layer soil amount from upper layer soil amount and divide theresult by the upper layer soil amount. (This option does not use the"quantity" or Action Code specified in standard line.)
SPEC-ACTIONS Block
739
User-defined variable quantity (UVQUAN)This option creates a variable quantity which can be used either as an action valuefor a Special Action or as a value to be compared in a condition. A UVQUAN refersto a single "base variable" in a single operation. By default, the UVQUAN containsthe last-calculated value of that base variable. Optionally, it may contain alagged value (e.g. 5 hours ago); an aggregated value (e.g. the average over theprevious day); or a combination (e.g. the sum over three days ending 24 hours ago).The resulting value can also be multiplied by a constant factor. It is importantto note the difference between a UVQUAN and a UVNAME. A UVQUAN is a value, justlike a constant. A UVNAME is a target address for a special action.
Logical conditionsSpecial Actions may depend on whether a user-specified logical condition is trueor false, and can be either skipped or deferred if it is false. They can begrouped into logical blocks by placing IF, ELSE IF, ELSE, and END IF linesappropriately among the action lines. For example, actions placed between an IFline and the next logical delimiter are executed only if the condition specifiedon the IF line is true on the date and time of the each action.
A simple logical condition is defined as a comparison between two numerical values.Either or both of these values may be UVQUANs. For example:
month <= 26226.0 <= tstagetfish < faradj
are all simple logical conditions. Complex conditions are built by connectingsimple conditions together with the logical operators AND and OR:
[month = 10 OR (tstage >= 6226.0 AND {month >= 11 OR month <= 2} )]
Parentheses are used to specify the order of evaluation, just as in a programminglanguage. By default, the logical operators are evaluated from right to left, butit is good practice to use them in all cases to ensure clarity. There are threetypes: round (), square [], and curly {}. They are equivalent, but the programrequires that matching left-right pairs must be of the same type, in order to helpthe user prevent unintended effects in complicated conditions.
IF lines consist of the keyword IF, a logical condition, and the keyword THEN. TheIF keyword may appear anywhere on the line, as long as it is the first non-blank.The condition may be simple or complex, and may span multiple physical lines. Whenan IF line is found, HSPF keeps reading lines until the THEN keyword is found.ELSE IF lines are processed in the same manner. ELSE and END IF are expected toappear alone on a line, and anything after the keyword is ignored. Note that the"ELSE IF" and "END IF" keywords must contain exactly one space between the twowords.
SPEC-ACTIONS Block
740
The following example illustrates how HSPF decides whether to perform a specialaction. Each condition may be simple or complex. Note the effect of nesting IF-END IF blocks. They may be nested up to ten levels deep.
*** Condition AIF month >= 9 THEN *** Action 1 RCHRES130 4 CVOL pw += TAVLQ *** Action 2 RCHRES130 4 CVOL tx -= TAVLQ
*** Condition B IF (tstage > 6226.0) THEN *** Action 3 RCHRES100 4 CVOL tx += PUNCOM
*** Condition C ELSE IF (tstage > 6225.0) THEN *** Action 4 RCHRES100 4 CVOL pw += PUNCOM
ELSE *** Action5 RCHRES130 4 CVOL na -= PUNCOM *** Action6 RCHRES130 4 CVOL ac += PUNCOM END IF
*** Action7 RCHRES130 4 CVOL sp += TAVLQ
*** Condition DELSE IF month >= 6 THEN *** Action8 RCHRES130 4 CVOL tc += TAVLQ
*** Condition EELSE IF (tfish <= 0.0) THEN *** Action9 RCHRES130 1991/04/29 12:00 4 CVOL pw += PUNCOM *** Action10 RCHRES130 1991/05/01 12:00 4 CVOL tx -= PUNCOM
ELSE *** Action11 RCHRES130 4 CVOL na -= TAVLQEND IF
*** Action12 RCHRES130 4 CVOL ac += TAVLQ
SPEC-ACTIONS Block
741
In this case:Actions 1, 2, and 7 are performed only if Condition A is true.Action 3 is performed only if Conditions A and B are both true.Action 4 is performed only if Conditions A and C are true and Condition B is false.Actions 5 and 6 are performed only if Condition A is true and Conditions B and Care false.Action 8 is performed only if Condition A is false and Condition D is true.Actions 9 and 10 are performed only if Conditions A and D are false and ConditionE is true.Action 11 is performed only if Conditions A, D, and E are false.Action 12 is always performed.
Evaluation OrderEach IF or ELSE IF line is evaluated a maximum of once per interval, at the timeof execution of the first Special Action that depends on it in that interval. TheUVQUAN values used for the numerical comparisons are computed from the basevariables at the point of evaluation, taking into account Special Actions appearingbefore the Condition line, but not those after it.
Assume that in the example above, month = 10, tstage = 6224.5, and tfish = 0. HSPFwill:
1) Fetch the value of month. 2) Evaluate Condition A as true. 3) Perform Action 1, since A is true. 4) Perform Action 2, since A is true. 5) Fetch the value of tstage. 6) Evaluate Condition B as false. 7) Skip Action 3, since B is false. 8) Re-fetch the value of tstage. 9) Evaluate Condition C as false.10) Skip Action 4, since C is false.11) Perform Actions 5 and 6, since A is true, while B and C are both false.12) Perform Action 7, since A is true.13) Skip Action 8, since A is true. Note that Condition D does not need to be
evaluated because the action is skipped regardless of D's value.14) Ignore Actions 9 and 10, since they do not occur on this date.15) Skip Action 11, since A is true. Conditions D and E can be ignored.16) Perform Action 12, which is unconditional.
The use of dated special actions within logical blocks requires caution. Forinstance, for Actions 9 and 10 above, it is possible that the value of tfishchanges between April 29 and May 1 such that one of the Actions is performed whilethe other is not, even though they have the same logical conditions. The user mustmake sure that this is the intended result.
Another situation requiring care is a Special Action that alters one of thevariables which define the logical conditions upon which that Action depends. Forinstance, if Action 4 above changed the value of the UVQUAN tstage to 6224.0 byaltering its base variable, then Actions 5 and 6 will still not be executed thatinterval, since Condition C is not re-evaluated until the following interval.
Monthly Data Block
742
4.11 MONTH-DATA Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout ******
MONTH-DATA
MONTH-DATA <t> <val-><val-><val-><val-><val-><val-><val-><val-><val-><val-><val-><val-> END MONTH-DATA <t>
Up to 50 MONTH-DATA tables may appear in the block
END MONTH-DATA
******* Example *******
MONTH-DATA
MONTH-DATA 3 *** atmospheric deposition fluxes (kg/ha/month) of NO3-N 1.3 1.5 2.0 2.1 2.2 2.2 3.0 2.3 2.0 2.0 1.7 1.4 END MONTH-DATA 3
END MONTH-DATA********************************************************************************
Details -------------------------------------------------------------------------------- Symbol FORTRAN Format Comment Name(s) --------------------------------------------------------------------------------<t> NUMBR I3 Users identifying number for this MONTH-DATA table
<val-> MTHVAL(12) 12F6.0 Monthly values-------------------------------------------------------------------------------
Explanation
A MONTH-DATA table is used to specify monthly-varying values for parameters thatdo not have specific input tables for that purpose. Currently, MONTH-DATA tablesare implemented for atmospheric deposition inputs. See descriptions for tabletypes PQL-AD-FLAGS, PEST-AD-FLAGS, etc. for further details.
Monthly Data Block
743
4.12 CATEGORY Block
******************************************************************************** 1 2 3 4 5 6 7 812345678901234567890123456789012345678901234567890123456789012345678901234567890********************************************************************************Layout******CATEGORY<cat> <----catnam---->. . . . . . . . . . . Above line repeats until all categories have been specified . . . . . . . . . . . END CATEGORY
Example *******
CATEGORY tag *** <> <----catnam----> *** UN UNCOMMITTED WP WESTPAC CREDIT CU CUI-UI CREDIT CA CALIF CREDIT TX TAHOE EXCHANGEEND CATEGORY
********************************************************************************
Details--------------------------------------------------------------------------------Symbol FORTRAN Start Format Comment Name(s) Column--------------------------------------------------------------------------------<cat> CAT 4 A2 Category tag: a two-character identifier used wherever a subscript is called for. First character must be a letter. Tags are case-sensitive, and must be unique.<catnam> CATNAM 7 A16 Category name--------------------------------------------------------------------------------
Explanation
In this block the user declares and names active water categories.
The CATEGORY block is used to facilitate the modeling of water rights in the HYDRsection of RCHRES. Each RCHRES in the run tracks the categories of all inflows,storages, demands, and outflows. Up to 100 categories may be specified. (See thediscussion of Water Rights Categories in Part E, Section 4.2(3).1).
Glossary
744
APPENDIX IGLOSSARY OF TERMS
1.0 NATURE OF THE GLOSSARY
The glossary which follows is not exhaustive. Its function is to introduce termswhich may be new and to assign definite meanings to ambiguous terms. It is not adictionary. The goal is not to provide formally correct definitions but to supplyexplanations adequate for practical purposes. Thus in some cases, the definitionof a term is followed by a further explanatory note.
2.0 GLOSSARY The list that follows is arranged alphabetically. Any word enclosed in parentheses( ) may optionally be omitted from a term in everyday use, provided that thecontext ensures that its use is implied. ACTUAL ARGUMENT The name of an item (or set) of data which is being passed to (or retrieved from)a subprogram via an argument list. It can be:
(1) a variable name (2) an array or array element name(3) any other expression(4) the name of an external procedure (5) a Hollerith constant
ANNIEAn interactive program designed for management of WDM files and their data. ANNIEfunctions include file creation, data set management, and data analysis,modification, and display.
APPLICATION MODULEA module which simulates processes which occur in the real world(e.g., PERLND, IMPLND, RCHRES). BUFFERA portion of machine memory space used for the temporary storage of input oroutput-bound data. COMPUTATIONAL ELEMENT See "element." CONCEPTUAL DATA STREAMA stream of related data that are independent of any physical input-output device.
Glossary
745
COPYA utility module used to copy time series data. COPY is typically used to transferdata from a sequential file to the WDM file or DSS file.
DATA SET (TIME SERIES) A data set in the WDM or DSS file. DIRECT ACCESS FILEA disk file whose records are read from or written to a specific location withinthe file. Any record in the file may be accessed at any time. Contrast withsequential file. DIRECTED GRAPHA group of processing units arranged with unidirectional paths between them. Nobi-directional paths or cycles are allowed. DISPLYA utility module used to print time series data and summaries of the data. DUMMY ARGUMENTThe local name (in a subprogram) for an actual argument which is passed to thesubprogram. DURANLA utility module used to examine the behavior of a time series, computing avariety of statistics related to its' excursions above and below certain specifiedlevels. ELEMENT A collection of nodes and/or zones, e.g. segment no. 1, reach no. 20. ELEMENT TYPEA name which describes elements having a common set of attributes, for example,Pervious Land-segment, Reach/Mixed Reservoir.
EXECUTABLE PROGRAMA self contained computing procedure. It consists of a main program and itsrequired subprograms. FEEDBACK ELEMENTAn element which is situated in a loop in a network or which is connected toanother element by one or more bi-directional flux linkages. FEEDBACK REGION A group of connected feedback elements. Information and constituent transfersacross the boundaries of a feedback region are uni-directional, but internal fluxescan be bi-directional. FLOWCHART A schematic two-dimensional representation of the logic in a program or programunit. The level of detail in a flowchart depends on its purpose.
Glossary
746
FLUXThe rate of transfer of fluid, particles or energy across a given surface. FUNCTION (as used in program design, not in Fortran language)A transformation which receives input and returns output in a predictable manner.Most functions within a program can be classified into one of three types: input,process, or output. Usually, there is a hierarchy of functions--high levelfunctions contain subordinate functions. GENER A utility module used to perform any one of several transformations on one or moreinput time series. IMPLNDAn application module which simulates the water quantity and quality processeswhich occur on an impervious land segment. INGRP A group of HSPF operations which share the same internal scratch pad (INPAD). INPAD see INTERNAL SCRATCH PAD INPAD AREAThe space available in memory for the storage of time series data in the INPAD.It is the difference between the area of the common block SCRTCH and the longestOSV in the INGRP. INPAD WIDTH The number of time intervals which are present in the INPAD during a run. This isthe INPAD area divided by the maximum number of rows of the time series data. HSPFuses fewer disk input/output operations with longer INPAD widths. INPUT TIME SERIES Time series which are read in a given simulation run. INSPANsee INTERNAL SCRATCH PAD SPAN INTER SECTION DATA TRANSFER (ISDT)The movement of information from one section to another within a module.
INTERNAL SCRATCH PAD (INPAD)The space in memory where time series data are accessed by modules. It functionsas a large buffer for this data. INTERNAL SCRATCH PAD SPAN (INSPAN)The real world time which corresponds to the INPAD width. IVL See SIMULATION INTERVAL
Glossary
747
JOB The work performed by HSPF in response to the instructions found in a complete setof User's Control Input. KINDA descriptor which implies either point or mean with regard to a time series.
MEAN VALUED DATAData which represents the behavior of a time series over time intervals rather thanat specific points in time. MIXED RESERVOIR A water body which is assumed to be completely mixed. MODEL A set of algorithms, set in a logical structure, which represents a process. Amodel is implemented using modules of code. MODULEA set of program units which performs a clearly defined function. MODULE SECTIONA part of an Application Module which can be executed independently of the otherparts. eg. SEDMNT in module PERLND. MUTSINA utility module used to read a sequential external file which has the same formatas the file produced by the PLTGEN module. MUTSIN makes the time series data on theexternal file available for use by other modules. NETWORK A group of connected processing units. Information and/or constituents flowbetween processing units through uni-directional linkages. That is, no processingunit may pass output which indirectly influences itself (no feedback loops). Theseconstraints make it possible to operate on each processing unit separately,considering them in an "upstream" to "downstream" order. NODEA point in space where the value of a spatially variable function can bedetermined.
OPERATING MODULE (OM) A set of HSPF program units which perform a series of process functions for aspecified time on a given set of input time series and produce a specified set ofoutput time series. OPERATION In HSPF: execution of code which transforms a set of input time series into a setof output time series, for example, execution of a application module or a utilitymodule. See "simulation operation," "utility operation."
Glossary
748
OPERATIONS STATUS VECTOR (OSV)The data structure for an operating module. The OSV contains all the information(parameters, state variables) needed to describe the status of an operation and torestart it after an interruption. OPERATIONS SUPERVISOR (OSUPER)The HSPF program units which oversee the execution of operating modules and relatedtime series movement. OSV see OPERATION STATUS VECTOR
OUTPUT TIME SERIESTime series which are generated during a simulation run. They do not have to bestored in the WDM or DSS. PARAMETER A variable used in a function which determines the transformation of the input tothe function to the output of the function.
PARTITION (an operation)The execution of different sections of an application module in separate runs.Time series involved in inter section data transfers must be stored between runs.
PERLNDAn application module which simulates the water quantity an quality processes whichoccur on a pervious land segment. PERVIOUS LAND SEGMENT (PLS) A segment of land with a pervious surface. PHYSICAL PROCESSA process occurring in the real world.
PLS see PERVIOUS LAND SEGMENT PLTGENA utility module used to write a sequential external file containing up to 10 timeseries and related commands for a stand alone plotting program. POINT VALUED DATA Data which represents the behavior of a time series at specific points in timerather than over time intervals. PROCESS In the real world: A continuing activity, for example, percolation, chemicalreaction. See "physical process."
Glossary
749
PROCESSING UNIT (PU)An element or group of related elements which is simulated for a period of time.Input comes from external sources or Processing Units which have completedsimulating for the given period of time. Output goes to other processing units ofexternal targets.
PROGRAM A complete set of code, consisting of one or more program units, the first of whichis the "main" program unit. PUsee PROCESSING UNIT RCHRESAn application module which simulates the water quantity and quality processeswhich occur in a reach of open or closed channel or a completely mixed lake.
REACH A free-flowing portion of a stream, simulated in HSPF using storage routing.
RUN A set of operations which are performed serially and cover the same period of time. RUN INTERPRETER The HSPF module which reads and interprets the User's Control Input. It sets upinternal information that instructs the system regarding the sequence of operationsto be performed, stores parameters and state variables for each operation in theOSV, writes instructions related to the movement of time series data and performsother minor functions. SECTION see MODULE SECTION SEGMENT A portion of the land assured to have areally uniform properties. SEQUENTIAL FILE A file whose records are organized on the basis of their successive physicalpositions, such as on magnetic tape or cards. A record may be accessed only afterthe previous record has been accessed. SIMPLE ELEMENTAn element which is not a feedback element. SIMULATIONImitation of the behavior of a prototype, using a model. We implement the modelon a computer using an application module. SIMULATION INTERVAL The internal time step used in an operation.
Glossary
750
SIMULATION MODULE See APPLICATION MODULE
SIMULATION (OPERATION)Simulation of a specified prototype for a specified period. STATE VARIABLEA variable containing the current value of a storage or other measurable quantity.It may change through time.
STRUCTURE CHART A diagram which documents the result of structured (program) design. It indicatesthe program units, their relationships (including hierarchy) and, optionally, thedata passed between them. SYSTEM DOCUMENTATIONA comprehensive set of documents which enable a user to understand and use asoftware product. It should include:
(1) a discussion of the underlying principles (2) a discussion of the mathematical relations which the code implements(3) documentation of the structure of the code(4) a listing of the code (5) documentation of data and file structures, including the input required
to run the program. TIME SERIES A series of chronologically ordered values giving a discrete representation of thevariation in time of a given quantity. (TIME SERIES) DATA SET A data set in the WDM or DSS file.
TIME SERIES MANAGEMENT SYSTEM (TSMS)The modules of HSPF which are concerned with manipulation of time series or thefiles used to store time series. It includes TSGET and TSPUT.
TIME SERIES STORE (TSS) A direct access file used for medium/long term storage of time series. Activemaintenance of the TSS system has ceased, and it has been removed from thedocumentation.
TIME SERIES STORE MANAGEMENT (TSSM) The HSPF module which (in previous versions) maintained a User's Time Series Store(TSS) and performed maintenance tasks associated with the data sets in it. TSSM hasbeen removed from the program and the documentation.
TSPUT The HSPF module which moves time series data from the INPAD to a WDM file or DSS. TSGET The HSPF module which moves time series data from a WDM file, DSS, or sequentialfile to the INPAD.
Glossary
751
TSS see TIME SERIES STORE TSSM or TSSMGRsee TIME SERIES STORE MANAGEMENT UCI see USER's CONTROL INPUT USER'S CONTROL INPUTThe file in which the user specifies the operations to be performed in a run, theparameters and initial conditions for each one, and the time series to be passedbetween them. HSPF reads this from an ASCII file. UTILITY MODULEA module which performs operations on time series which are peripheral to thesimulation of physical processes, for example, data input, plot generation,statistical analysis.
UTILITY OPERATION Execution of a utility module. VOLUMEA source (WDM, DSS, sequential file or INPAD) or target (WDM or DSS) for the timeseries data. Watershed Data Management (WDM) FileA direct-access, binary file containing multiple time series data sets. This fileis the primary storage file for HSPF time series data. WDM files are created andmaintained by the ANNIE program and related-software.
WORLD VIEWA representation of the real world which includes simplifying assumptions ofphysical processes.
ZONEA finite portion of the real world. It is usually associated with the integral ofa spatially variable quantity.
Time Series Concepts
752
APPENDIX IITIME SERIES CONCEPTS
1.0 Time Series Concepts
A time series is a sequence of values ordered in time. The interval of timebetween successive values is called the time step or the time increment or the timeinterval of the time series. The time step for a time series is often a constantvalue but may also be variable. The implementation in HSPF restricts thevariability in a manner discussed below. The values in the time series mayrepresent the behavior of a process at a point in time or an average over the timestep of the time series. A time series whose values represent behavior at pointsin time is called a point-valued time series and is represented symbolically by"*". Linear interpolation is used to define intermediate values in a point-valuedtime series. A time series whose values represent average or aggregated behaviorover the time step are called mean-valued time series and are representedsymbolically as "-". The meaning of "average" and "mean" is taken in a wide senseand includes any value assumed to be representative of behavior of the time seriesover the time step, rather than at a specific point in time. The following figure shows the difference between the point and mean value timeseries in graphic form. It is important to note that only one value is needed torepresent the behavior of a mean-valued time series for one time step. We visualizethe value as being assigned to a time step in this case. On the other hand, twovalues are needed to represent the behavior of a point-valued time series over thesame interval. We visualize the values as being assigned to the time points in thiscase. Each time point at which a value of the series is given in a point-valuedtime series is viewed as "belonging" to the time step which it ends. Time pointsbelonging to all time steps contained within a larger time step are viewed asbelonging to the larger time step also. For example, all time points in apoint-valued time series except the first time point belong to the time intervalspanning the time series duration. The first time point of a point-valued timeseries is viewed as belonging to the time step immediately preceding the first timestep of the time series. This precise definition of belongingness for a time pointis needed to avoid confusion in defining operations on the time series. A number of operations on time series, discussed in Section 4.6 of Part F, preservethe integral of the time series between any two time points which end time stepsin the time series. The integral may be visualized as the area under the brokenline graph formed by connecting adjacent values in the point-valued time series orthe area under the histogram representing the mean-valued time series. Thetrapezoidal rule applied to the point-valued time series yields the exact value ofthe integral whereas the simple rectangular rule yields the exact value for themean-valued time series.
Time Series Concepts
753
Point-valued time series | | | | | | X | o | | o o | | o | o o o | | | |------|------|------|------|------|------|------ 0 1 2 3 4 5 6 Time point numbers Mean-valued time series | | | | | X | .------. | | | |------. .------' | | | | | | '------' | | '-------------. | | | | |------|------|------|------|------|------|----- Time point # --> 0 1 2 3 4 5 6 Time step # --> 1 2 3 4 5 6 - - - - - - Figure 1. Comparison between point- and mean-valued time series
Time Series Concepts
754
Time is given as year/month/day/hour/minute to completely specify either a timeinterval or a time point. The date/time given by the internal clock uses the"contained within" principle for all levels of the date/time. That is, eachsmaller interval is contained within the next larger interval. This is theconventional usage for year/month/day but is not conventional for the hour/ minute.For example, the date string 1977/01/02 labels the second day of the first monthof the 1977th year. On the other hand, in conventional usage the time string 10:15refers to the end of the l5th minute after (not within) the 10th hour of the day.This change in meaning is eliminated in the internal date/ time clock for HSPF.In the internal system, time string 10/15 labels the 15th minute of (ie. within)the 10th hour of the day. A comparable time to 10:15 in the conventional sensewould be 11/15; that is, the 15th minute of the 11th hour of the day. In summary, the internal clock convention labels time intervals at all levels ofdate/time whereas conventional usage labels time intervals for year/month/day butlabels time points for hour/minute. In HSPF, time points are then referenceduniquely by the minute which ends at the time point in question. The time steps in a time series are labelled with the minute which ends the timestep. Thus, the values in a mean-valued time series are treated logically ashaving occurred at the end time point of the time step. Note that for purposes ofthe internal clock and for description of internal concepts each time point has oneand only one label. This means that we refer to the instant in time forming theboundary between two days using the label associated with the first day even thoughour interest is centered on the second day. This convention is called the endingtime convention. A starting time convention is used externally for some purposes because traditionalusage requires both conventions depending on the context of the statement abouttime. Users are more comfortable using the traditional clock and both a startingtime and an ending time convention. The starting time convention is used when thestart of some time span is in mind and the ending time convention is used when theend of some time span is in mind. The time span associated with a time series must be defined. Logically, a timeseries is of infinite length. Realistically, every time series has a finite lengthand may be broken into short segments for convenience in recording the values onsome medium such as the printed page, a magnetic tape, a data card or a magneticdisk. These shorter segments are made necessary by various software and hardwareconstraints. Therefore, a time span is associated with each medium used to recordor store the time series.
Time Series Concepts
755
A further practical complication is created by the variety of representations usedfor time series. The user's most likely mental image is a line drawn in somecoordinate system on the printed page. This method of representing time series ismost convenient for the user but a series of discrete numbers is most convenientfor the digital computer. The time series of indefinite length must be subdividedinto shorter time spans to fit the card images or the records on the tape or disk.In some cases data for the time series may be incomplete (some values not present)or, in some cases, many of the values are zero so that not all values for the timeseries are stored on the medium. In such cases a date/time indicator is given onthe record. As an example, think of the format used for data cards punched by theNational Weather Records Center. The date/time information on each record of themedium permits the reconstruction of the complete time series (except for themissing values) even though not all values are recorded on the medium. However,conventions must be established so that missing records on a given recording mediumare properly interpreted. For example, are the missing data merely zeros or didthey occur because of instrument malfunction? If the data are missing, a "filler"should be inserted when the data are placed on the WDM or DSS so that it can bechanged at a later time or so that such missing periods can be properly handled byother parts of the HSPF system. The filler value is called the TSFILL attribute inthe WDM system. The time step for a time series can vary in multiples of a basic time stepestablished for the time series. The basic time step for the time series must betruly a constant value. For example, a time series at a monthly interval does nothave a constant time step. Therefore, the basic time step assigned to such a timeseries is daily because a day is of constant length and is commensurable with allmonths.
1
BIBLIOGRAPHY FOR HSPF AND RELATED REFERENCES
prepared by
A.S. Donigian, Jr.
AQUA TERRA ConsultantsMountain View, CA 94086
May 31, 1994
Ball, J.E., M.J. White, G.de R. Innes, and L. Chen. 1993. Application of HSPF on the UpperNepean Catchment. Hydrology and Water Resources Symposium, Newcastle, New SouthWales, Australia. June 30-July 2, 1993. pp. 343-348.
Barnwell, T.O. 1980. An Overview of the Hydrologic Simulation Program - FORTRAN, aSimulation Model for Chemical Transport and Aquatic Risk Assessment. In: AquaticToxicology and Hazard Assessment: Proceedings of the Fifth Annual Symposium on AquaticToxicology, ASTM Special Tech. Pub. 766, ASTM, 1916 Race Street, Philadelphia, PA 19103.
Barnwell, T.O. and R. Johanson. 1981. HSPF: A Comprehensive Package for Simulation ofWatershed Hydrology and Water Quality. In: Nonpoint Pollution Control: Tools andTechniques for the Future. Interstate Commission on the Potomac River Basin, 1055 FirstStreet, Rockville, MD 20850.
Barnwell, T.O. and J.L. Kittle. 1984. "Hydrologic Simulation Program - FORTRAN:Development, Maintenance and Applications." In: Proceedings Third International Conferenceon Urban Storm Drainage. Chalmers Institute of Technology, Goteborg, Sweden.
Beyerlein, D.C. 1993. May Creek Conditions Report (Hydrology and Flooding Chapters).Prepared for King County Surface Water Management and the City of Renton Surface WaterUtility, Seattle, WA. Prepared by AQUA TERRA Consultants, Everette, WA.
Beyerlein, D.C. 1993. Crisp Creek Watershed Report. Prepared for Muckleshoot Indian TribeFisheries Department, Auburn, WA. Prepared by AQUA TERRA Consultants, Everette, WA.
Beyerlein, D.C. and J.T. Brasher. 1994. Chambers Creek Calibration Report. Prepared forThurston County Storm and Surface Water Programs. Olympia, WA. Prepared by AQUATERRA Consultants, Everette, WA.
Beyerlein, D.C. and J.T. Brasher. 1994. May Creek Calibration Report. Prepared for KingCounty Surface Water Management and the City of Renton Surface Water Utility, Seattle, WA. Prepared by AQUA TERRA Consultants, Everette, WA.
Bicknell, B.R., A.S. Donigian Jr. and T.O. Barnwell. 1984. Modeling Water Quality and theEffects of Best Management Practices in the Iowa River Basin. J. Water. Sci. Tech., 17:1141-1153.
Bicknell, B.R., J.C. Imhoff, J.L. Kittle Jr., A.S. Donigian, Jr. and R.C. Johanson. 1993.
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Hydrological Simulation Program - FORTRAN. User's Manual for Release 10. EPA/600/R-93-174. U.S. EPA Environmental Research Laboratory, Athens, GA.
Bowie, G.L., W.B. Mills, D.B. Porcella, C.L. Campbell, J.R. Pagenkopf, G.L. Rupp, K.M.Johnson, P.W.H. Chan, and S.A. Gherini. 1985. Rates, Constants, and Kinetics Formulations inSurface Water Quality Modeling (Second Edition). Environmental Research Laboratory,Athens, GA. EPA 600/3-85/040. 455 p.
Casman, E. 1989. Effects of Agricultural Practices on Water Quality as Related to Adjustmentsof HSPF Parameters, A Literature Review. Report No. 89-6. Interstate Commission on thePotomac River Basin, in cooperation with the Maryland Department of the Environment,Baltimore, MD.
Chew, C.Y. L.W. Moore, and R.H. Smith. 1991. Hydrological Simulation of Tennessee's NorthReelfoot Creek Watershed. Res. J. WPCF 63(1):10-16.
Codner, G.P., 1991. Tale of Two Models, SWMM and HSPF. International Hydrology andWater Resources Symposium, Part 2 (of 3), Perth, Australia. p 569-574.
Colglazier, E.W. 1989. Fiscal Year 1989 Program Report ( Tennessee Water Resources ResearchCenter). (Available from) National Technical Information Service, Springfield, VA. (HSPFApplication to North Reelfoot Creek Watershed in Tennessee).
Dames and Moore. 1988. Yiba, Tabalah, and Habawnah Basins Hydrological ModellingReports. Prepared for Kingdom of Saudi Arabia, Ministry of Agriculture and Water, Riyadh,Saudi Arabia.
Dean, J.D., D.W. Meier, B.R. Bicknell and A.S. Donigian, Jr. 1984. Simulation of DDTTransport and Fate in the Arroyo Colorado Watershed, Texas, Draft Report, prepared for U.S.EPA, Environmental Research Laboratory, Athens, GA.
Dinicola, R.S. 1990. Characterization and Simulation of Rainfall-Runoff Relations forHeadwater Basins in Western King and Snohomish Counties, Washington. Water-ResourcesInvestigation Report 89-4052. U.S. Geological Survey, Tacoma, WA.
Donigian, A.S., Jr., and N.H. Crawford. 1976a. Modeling Pesticides and Nutrients onAgricultural Lands. Environmental Research Laboratory, Athens, GA. EPA 600/2-7-76-043. 317 p.
Donigian, A.S., Jr., and N.H. Crawford. 1976b. Modeling Nonpoint Pollution from the LandSurface. Environmental Research Laboratory, Athens, GA. EPA 600/3-76-083. 280 p.
Donigian, A.S. Jr., D.C. Beyerlein, H.H. Davis, Jr., and N.H. Crawford. 1977. AgriculturalRunoff Management (ARM) Model Version II: Refinement and Testing. EnvironmentalResearch Laboratory, Athens, GA. EPA 600/3-77-098. 294 pp.
Donigian, A.S., Jr., J.C. Imhoff and B.R. Bicknell. 1983. Modeling Water Quality and the Effectsof Best Management Practices in Four Mile Creek, Iowa. EPA Contract No. 68-03-2895,
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Environmental Research Laboratory, U.S. EPA, Athens, GA. 30613.
Donigian, A.S. Jr., Baker, D.A. Haith and M.F. Walter. 1983. HSPF Parameter Adjustments toEvaluate the Effects of Agricultural Best Management Practices, EPA Contract No. 68-03-2895,U.S. EPA Environmental Research Laboratory, Athens, GA, (PB-83-247171).
Donigian, A.S. Jr., J.C. Imhoff and B.R. Bicknell. 1983. Predicting Water Quality Resultingfrom Agricultural Nonpoint Source Pollution via Simulation - HSPF, In: AgriculturalManagement and Water Quality, (ed.) F.W. Schaller and G.W. Baily, Iowa State UniversityPress, Ames, IA, pp. 209-249.
Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell and J.L. Kittle, Jr. 1984. Application Guide for theHydrological Simulation Program - FORTRAN EPA 600/3-84-066, Environmental ResearchLaboratory, U.S. EPA, Athens, GA. 30613.
Donigian, A.S., Jr., D.W. Meier and P.P. Jowise. 1986. Stream Transport andAgricultural Runoff for Exposure Assessment: A Methodology. EPA/600/3-86-011,
Environmental Research Laboratory, U.S. EPA, Athens, GA. 30613.
Donigian, A.S. 1986. Integration of Runoff and Receiving Water Models for Comprehensive Watershed Simulation and Analysis of Agricultural Management Alternatives, In: Agricultural Nonpoint Source Pollution: Model Selection and Application. Developments in Environmental Modeling, 10, Elsevier, New York. p. 265-275.
Donigian, A.S. Jr., B.R. Bicknell and J.L. Kittle Jr. 1986a. Conversion of the ChesapeakeBay Basin Model to HSPF Operation. Prepared by AQUA TERRA Consultants for the
Computer Sciences Corporation, Annapolis, MD and U.S. EPA Chesapeake Bay Program,Annapolis, MD.
Donigian, A.S., Jr., B.R. Bicknell, L.C. Linker, J. Hannawald, C. Chang, and R. Reynolds. 1990. Chesapeake Bay Program Watershed Model Application to Calculate Bay Nutrient Loadings: Preliminary Phase I Findings and Recommendations. Prepared by AQUA TERRA Consultantsfor U.S. EPA Chesapeake Bay Program, Annapolis, MD.
Donigian, A.S. Jr., B.R. Bicknell, A.S. Patwardhan, L.C. Linker, D.Y. Alegre, C.H.Chang and R. Reynolds. 1991. Chesapeake Bay Program Watershed Model
Application to Calculate Bay Nutrient Loadings. Prepared by AQUA TERRA Consultants forU.S. EPA Chesapeake Bay Program , Annapolis, MD.
Donigian, A.S., Jr. and W.C. Huber. 1991. Modeling of Nonpoint Source Water Quality inUrban and Non-Urban Areas. EPA/600/3-91/039. U.S. Environmental Protection Agency,Environmental Research Laboratory, Athens, GA. 72 p.
Donigian, A.S. Jr., and A.S. Patwardhan. 1992. Modeling Nutrient Loadings from Croplands inthe Chesapeake Bay Watershed. In: Proceedings of Water Resources sessions at Water Forum'92. Baltimore, Maryland. August 2-6, 1992. p. 817-822.
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Donigian, A.S. Jr., R.V. Chinnaswamy, and D.C. Beyerlein. 1993. Surface Water ExposureAssessment for Walnut Creek, Iowa - Preliminary Application of the U.S. EPA HSPF to AssessAgrichemical Contributions and Impacts. Prepared by AQUA TERRA Consultants, MountainView, CA. Prepared for U.S. EPA, Athens , GA under Contract No. 68-C0-0019.
Donigian, A.S. Jr., B.R. Bicknell, and J.C. Imhoff. 1994. Hydrological Simulation Program -FORTRAN (HSPF). Chapter 12. In: Computer Models of Watershed Hydrology. V.P. Singh(ed). Water Resources Publications. Littleton, CO. (In Press).
Fielland, C.E. and M.A. Ross. 1990. An Input Preprocessor Linked to a GIS for HSPFHydrologic Simulation. Department of Civil Engineering and Mechanics, University of SouthFlorida, Tampa, FL. Presented at National Conference on Microcomputers in CivilEngineering, Orlando, FL
Fielland,C.E. and M.A. Ross. 1991. Improved HSPF Infiltration Calibration Procedure with aLinked GIS. Tampa Port Authority, Tampa, FL.
Fisher, G.T. 1989. Geographic Information System/Watershed Model Interface, In: Hydraulic Engineering '89 Proceedings, National Conference on Hydraulic Engineering, HY Div, ASCE, New Orleans, LA, August 14-18, 1989. p. 851-856.
Fisher, I.H., M. Rahman, J.T. Jivajirajah, and I. Salbe. 1993. HSPF Water Quality Modelling onSouth Creek. Hydrology and Water Resources Symposium, Newcastle, New South Wales,Australia. June 30-July 2, 1993. pp. 355-360.
Franz, D.D. and S.M. Lieu. 1980. Evaluation of Remote Sensing Data for Input IntoHydrological Simulation Program- FORTRAN (HSPF). Prepared by Hydrocomp, Inc. PaloAlto, CA. Prepared for U.S. EPA, Athens, GA under Contract No. 68-01-5801.
Gilbert, D.P., D. Soboshinski, G Bartelt, and D. Razavian. 1982. Development of State WaterQuality Management Plan for the State of Nebraska, Nebraska Water Resources Center,University of Nebraska, 310 Agricultural Hall, Lincoln, NB.
Hicks, C.N., W.C. Huber and J.P. Heaney. 1985. Simulation of Possible Effects of DeepPumping on Surface Hydrology Using HSPF. In: Proceedings of Stormwater and WaterQuality Model User Group Meeting. January 31 - February 1, 1985. T.O. Barnwell, Jr., ed. EPA-600/9-85/016. Environmental Research Laboratory, Athens, GA.
Imhoff, J.C., B.R. Bicknell, and A.S. Donigian, Jr. 1983. Preliminary Application of HSPF to theIowa River Basin to Model Water Quality and the Effects of Agricultural Best ManagementPractices, Office of Research and Development, U.S. Environmental Protection Agency,Contract No. 68-03-2895, (PB-83-250399).
Johanson, R.C., J.C. Imhoff and H.H. Davis. 1980. User's Manual for the HydrologicSimulation Program - FORTRAN (HSPF). Version No. 5.0. EPA-600/9-80-105. U.S.
EPA Environmental Research Laboratory, Athens, GA.
Johanson, R.C. and D.Kliewer. 1980. Maintenance and Testing of Hydrological SimulationProgram-FORTRAN (HSPF). Prepared by Hydrocomp, Inc. Palo Alto, CA. Prepared for U.S.
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EPA, Athens, GA under Contract No. 68-01-5801.
Johanson, R.C., J.C. Imhoff and H.H. Davis, J.L. Kittle, and A.S. Donigian, Jr. 1981. User'sManual for the Hydrologic Simulation Program - FORTRAN (HSPF): Version No. 7.0. Prepared for U.S. EPA Environmental Research Laboratory, Athens, GA. Prepared byAnderson-Nichols & Co. Inc., Palo Alto, CA.
Johanson, R.C. 1983. A New Mathematical Modeling System. In: Fate of Chemicals In TheEnvironment Am.Chem. Soc. Symp. Series No. 225. Washington, D.C.
Johanson, R.C. and J.L. Kittle. 1983. "Design, Programming and Maintenance of HSPF", Journal of Technical Topics in Civil Engineering, Vol. 109, No. 1
Johanson, R.C., J.C. Imhoff, J.L. Kittle, Jr. and A.S. Donigian. 1984. Hydrological SimulationProgram - FORTRAN (HSPF): Users Manual for Release 8.0, EPA-600/3-84-066,Environmental Research Laboratory, U.S. EPA, Athens, GA. 30613.
Johanson, R.C., A.S. Donigian, Jr. and T.O. Barnwell. 1984. Modeling Transport andDegradation of Pesticides in the Soil and Surface Environments, In: Prediction of PesticideBehavior in the Environment, Proceedings U.S. - U.S.S.R. Symposium, Yerevan, Armenia,U.S.S.R., October 1981, EPA-600/9-84-026.
Johanson, R.C. 1989. Application of the HSPF Model to Water Management in Africa. In: Proceedings of Stormwater and Water Quality Model Users Group Meeting. October 3-4, 1988. Guo, et al., eds. EPA-600/9-89/001. Environmental Research Laboratory, Athens, GA.
Johanson, R.C. 1989. Water Quantity/Quality Modelling in an Overseas Situation. Proc. ofSpecialty Conference of ASCE, Water Resources Planning and Management Division,Sacramento, CA.
King County Surface Water Management. 1990. East Lake Sammamish Basin ConditionsReport -- Preliminary Analysis. Seattle, WA.
King County Surface Water Management. 1993. Cedar River Basin Current and FutureConditions Technical Report. Seattle, WA.
Kolomeychuk R. and B. Kalanda. 1983. Simulation of Fecal Coliform Bacteria Using HSPFIn: Colloque sur la Modelisation des Eaux Pluviales. Septembre 8-9, 1983. P. Beron, etal., T. Barnwell, editeurs. GREMU - 83/03 Ecole Polytechnique de Montreal, Quebec,Canada.
Kittle, J.L. 1983. Use of the Hydrological Simulation Program - FORTRAN on the HP1000 Microcomputer, In: Emerging Computer Techniques in Stormwater and Flood Management. W. James (ed). Lewis Publishers, Boca Raton, FL. p. 210-222
Linker, L.C., G.E. Stigall, C.H. Chang, and A.S. Donigian, Jr. 1993. The Chesapeake Bay
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Watershed Model. Prepared by U.S. EPA, Chesapeake Bay Program Office, Annapolis, MD.
Lorber, M.N. and L.A. Mulkey. 1982. An Evaluation of Three Pesticide Runoff LoadingModels. J. Environ. Qual. 11:519-529.
Lukas, A.B. and C.M. Cary. 1993. City of Federal Way, Panther Lake Surface Water Study -A Case Study of HSPF Modeling in Urban Detention Basins, In: Proceedings of the 20thAnniversary Conference on Water Management in the 90's, Seattle, Washington. p. 185-188.
Lumb, A.M., J.L. Kittle, and K. M. Flynn. 1990. Users Manual for ANNIE, A ComputerProgram for Interactive Hydrologic Analyses and Data Management, Water-ResourcesInvestigations Report 89-4080, U.S. Geological Survey, Reston, VA.
Lumb, A.M., and J.L. Kittle, Jr.. 1993. "Expert System for Calibration and Application ofWatershed Models," In: Proceedings of the Federal Interagency Workshop on HydrologicModeling Demands for the 90's. U.S. Geological Survey Water Resources Investigation Report93-4018. J.S. Burton, ed.
Lumb, A.M. 1993. "Integrating New Technologies to Support Watershed ManagementDecisions - A USGS Perspective," In: Developing Dynamic Watershed Loadings ModelingCapabilities for Great Lakes Tributaries. The Proceedings of an EPA Workshop at HeidelbergCollege in Tiffin, Ohio, August 4-6, 1993. Office of Research and Development, U.S.Environmental Protection Agency, Athens, GA. J.C. Imhoff, ed.
MacLaren Plansearch. 1984. Snow Hydrology Study. Phases I and II: Study Methodology andSingle Event Simulation. Report prepared for Ontario Ministry of Natural Resources, Ontario,Canada. Prepared by MacLaren Plansearch, Toronto, Canada.
Manitoba River Forecast Development. Volume I: Phase I, Palnning and Design -- Volume 2: Phase II, Application and Evaluation. Manitoba Dept. of Natural Resources, Winnipeg, Canada.
Moore, L.W., H. Matheny, T. Tyree, D. Sabatini, and S.J. Klaine. 1988. Agricultural RunoffModeling in a Small West Tennessee Watershed. J. Water Pollution Control Federation60(2):242-249.
Moore, L.W. C.Y. Chew, R.H. Smith,and S.Sahoo. 1992. Modeling of Best ManagementPractices on North Reelfoot Creek, Tennessee. Water Env. Res. 64(3):241-247.
Motta, D.J. and M.S. Cheng. 1987. "The Henson Creek Watershed Study" In: Proceedings ofStormwater and Water Quality Users Group Meeting. October 15-16, 1987. H.C. Torno, ed. Charles Howard and Assoc., Victoria, BC, Canada.
Mulkey, L.A. and A.S. Donigian, Jr. 1984. Modeling Alachlor Behavior in Three AgriculturalRiver Basins, U.S. EPA, Environmental Research Laboratory, Athens, GA.
Mulkey, L.A., R.B. Ambrose, and T.O. Barnwell. 1986. "Aquatic Fate and Transport Modeling
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Techniques for Predicting Environmental Exposure to Organic Pesticides and Other Toxicants -A Comparative Study." In: Urban Runoff Pollution, Springer-Verlag, New York, NY.
Mulkey, L.A., R.F. Carsel, and C.N. Smith. 1986. Development, Testing, and Applications ofNonpoint Source Models for Evaluation of Pesticides Risk to the Environment, In: AgriculturalNonpoint Source Pollution: Model Selection and Application. Developments in EnvironmentalModeling, 10, Elsevier, New York. p. 383-397.
Nath, A.K. 1986. Impact of Extensive Irrigation Pumpage on Streamflow by HSPF,In: Proceedings of Stormwater and Water Quality Model Users Group Meeting, March,
1986. NTIS, Springfield, VA. p. 93-108
Ng, H.Y.F. and J. Marsalek. 1989. Simulation of the Effects of Urbanization on Basin Streamflow. Water Resources Bulletin WARBAQ, Vol. 25, No. 1, p. 117-124.
Nichols, J.C. and M.P. Timpe. 1985. Use of HSPF to simulate Dynamics of Phosphorus inFloodplain Wetlands over a Wide Range of Hydrologic Regimes. In: Proceedings ofStormwater and Water Quality Model Users Group Meeting, January 31 - February 1, 1985. T.O. Barnwell, Jr., ed. EPA-600/9-85/016, Environmental Research Laboratory, Athens, GA.
Northern Virginia Planning District Commission. 1983. Chesapeake Bay Basin Model. FinalReport Prepared for U.S. EPA Chesapeake Bay Program, Annapolis, MD.
Perrens, S., B. Druery, B. le Plastrier, J. Nielsen, G.S. Greentree, and I. Fisher. 1991. Nepean-Hawkesbury River Water Quality Modeling. International Hydrology and Water ResourcesSymposium, Part 2 (of 3), Perth, Australia. Schafer, D.E., D.A. Woodruff, R.J. Hughto and G.K. Young. 1982. "Calibration of
Hydrlogy and Sediment Transport on Small Agricultural Watersheds using HSPF", in Proceedings of Stormwater and Water Quality Management Modeling Users Group Meeting, 25-26 March 1982, EPA-600/9-82-015, U.S. EPA, Athens, GA 30613, pp54-
68.
Scheckenberger, R.B. and A.S. Kennedy. 1994. The Use of HSP-F in Subwatershed Planning, In: Current Practices in Modelling the Management of Stormwater Impacts. W. James (ed). Lewis Publishers, Boca Raton, FL. p. 175-187.
Schnoor, J.L., C. Sato, D. McKetchnie, and D. Sahoo. 1987. Processes, Coefficients, and Modelsfor Simulating Toxic Organics and Heavy Metals in Surface Waters. EPA/600/3-87/015. U.S.Environmental Protection Agency, Athens, GA. 30613.
Schueler, T.R. 1983. Seneca Creek Watershed Management Study, Final Report, Volumes Iand II. Metropolitan Washington Council of Governments, Washington, DC.
Schueler, T.R. and M.P. Sullivan. 1983. Management of Stormwater and Water Quality in anUrbanizing Watershed. Presented at 1983 International Symposium on Urban Hydrology,Hydraulics, and Sediment Control. University of Kentucky, Lexington, KY. July 25-28, 1983.pp. 221-230.
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Smith, R.H., S.N. Sahoo, and L.W. Moore. 1992. GIS Bases Synthetic Watershed SedimentRouting Model. In: Proceedings of Water Resources sessions at Water Forum '92. Baltimore, Maryland. August 2-6, 1992. p. 200-207.
Snohomish County Surface Water Management. 1993. North Creek Watershed ManagementPlan, Public Hearing Draft. Everett, WA.
Snohomish County Surface Water Management. 1994. Swamp Creek Watershed ManagementPlan, Public Hearing Draft. Everett, WA.
Song, J.A., G.F. Rawl, W.R. Howard. 1983. Lake Manatee Watershed Water ResourcesEvaluation using Hydrologic Simulation Program - FORTRAN (HSPF) In: Colloque sur laModelisation des Eaux Pluviales. Septembre 8-9, 1983. P. Beron, et al., T. Barnwell, editeurs. GREMU - 83/03 Ecole Polytechnique de Montreal, Quebec, Canada.
Sullivan, M.P. and T.R. Schueler. 1985. Simulation of the Stormwater and Water QualityAttributes of Ponds with HSPF. In: Proceedings of Stormwater and Water Quality ModelUsers Group Meeting, April 12 - 13, 1984. T.O. Barnwell, Jr., ed. EPA-600/9-85/003,Environmental Research Laboratory, Athens, GA. pp. 147-162.
Sullivan, M.P. and T.R. Schueler. 1982. The Piscataway Creek Watershed Model: AStormwater and Nonpoint Source Management Tool. In: Proceedings Stormwater and WaterQuality Management Modeling and SWMM Users Group Meeting, October 18-19, 1982. PaulE. Wisner, ed. Univ. of Ottawa, Dept. Civil Engr., Ottawa, Ont., Canada.
Weatherbe, D.G. and Z. Novak. 1985. Development of Water Management Strategy for theHumber River. In: Proceedings Conference on Stormwater and Water Quality ManagementModeling, September 6-7, 1984. E.M. and W. James, ed. Computational Hydraulics Group,McMaster University, Hamilton, Ont., Canada.
Woodruff, D.A., D.R. Gaboury, R.J. Hughto and G.K. Young. 1981. Calibration of PesticideBehavior on a Georgia Agricultural Watershed Using HSP-F. In: Proceedings Stormwater andWater Quality Model Users Group Meeting, September 28-29, 1981. W. James, ed. Computational Hydraulics Group, McMaster University, Hamilton, Ont., Canada.
Udhiri, S., M-S Cheng and R.L. Powell. 1985. The Impact of Snow Addition on WatershedAnalysis Using HSPF. In: Proceedings of Stormwater and Water Quality Model Users GroupMeeting, January 31 - February 1, 1985. T.O. Barnwell, Jr., ed. EPA-600/9-85/016,Environmental Research Laboratory, Athens, GA.
Xie, J.D.M. and W. James. 1993. Modelling Solar Thermal Enrichment of Urban Stormwater. In:Current Practices in Modelling the Management of Stormwater Impacts. W. James (ed). LewisPublishers, Boca Raton, FL. p. 205-217.
Yang, J.C., H.P. Lee, and J.H. Chang. 1992. Pollutant Transport Modelling for a ComplexRiver System, In: Computer Techniques and Applications. Hydraulic Engineering SoftwareIV. Computational Mechanics and Publications. Southampton, England, and ElsevierApplied Science, London, England. p. 49-60.
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HSPF Application Citations Provided by Northwest Hydraulic Consultants, Inc., Kent,Washington.
Northwest Hydraulic Consultants Inc., Beaverdam Master Drainage Plan Hydrologic Analysis. October 1992. Prepared for Hugh G. Goldsmith & Associates.
Northwest Hydraulic Consultants Inc., Beaverdam Master Drainage Plan HSPF ModelCalibration. June 1992. Prepared for Hugh G. Goldsmith & Associates.
Northwest Hydraulic Consultants Inc., Northridge Phase I Master Drainage Plan HydrologicAnalysis. December 1991. Prepared for Hugh G. Goldsmith & Associates.
Northwest Hydraulic Consultants Inc., Northridge Phase I Master Drainage Plan HSPF ModelCalibration. December 1991. Prepared for Hugh G. Goldsmith & Associates.
Northwest Hydraulic Consultants Inc., Sea-Van Development Storm Water Detention FacilitiesDesign Procedures. April 1992. Prepared for R. W. Beck and Associates, 2101 4th Avenue,Suite 600, Seattle, WA 98121.
Northwest Hydraulic Consultants Inc., Eastside Green River Watershed Hydrologic Analysis. December 1991. Prepared for R. W. Beck & Associates and City of Renton Department ofPublic Works.
Northwest Hydraulic Consultants Inc., City of Lynnwood Comprehensive Drainage PlanHydrologic Modeling. May 1990. Prepared for R.W. Beck & Associates.
Northwest Hydraulic Consultants Inc., Mill Creek (Auburn) Hydrologic Modeling andAnalysis. November 1993. Prepared for King County Division of Surface Water Management,City of Auburn, City of Kent.
Northwest Hydraulic Consultants, Inc., Scriber Creek Watershed Management Plan. December 1989. As subconsultants to R. W. Beck. Prepared for Snohomish County PublicWorks Surface Water Management, City of Lynnwood Department of Public Works, and Cityof Brier Department of Public Works.
Northwest Hydraulic Consultants Inc., Mill Creek (Auburn) Hydrologic Modeling andAnalysis. November 1993. Prepared for King County Division of Surface Water Management,City of Auburn, City of Kent.
Northwest Hydraulic Consultants Inc., Mill Creek (Auburn) HSPF Model Calibration. July1993. Prepared for King County Division of Surface Water Management, City of Auburn, Cityof Kent.
Northwest Hydraulic Consultants Inc., Analysis of Lagoon Performance and Impact on Flowsin Lower Mill Creek. June 1992. Prepared for City of Kent.
Northwest Hydraulic Consultants Inc., Stormwater Modeling for Horseshoe Acres Pump
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Station. May 1992. Prepared for City of Kent.
Northwest Hydraulic Consultants Inc., Hydrologic and Hydraulic Analyses for StormwaterDetention for the Proposed South 5th Avenue Green River Outlet. June 1991. Prepared for City of Kent, 220 4th Avenue South Kent, Washington, 98032.
Northwest Hydraulic Consultants Inc., Combined Stormwater Detention/Enhanced WetlandFacility Recommended Plan Hydrology and Hydraulics, Part III - Kent Detention Analysis. November 1990. Prepared for City of Kent, 224 4th Avenue, Kent, 98032.
Northwest Hydraulic Consultants Inc., Combined Stormwater Detention/Enhanced WetlandFacility Recommended Plan Hydrology & Hydraulics, Part III. November 1990. Prepared forCity of Kent.
Northwest Hydraulic Consultants Inc., Combined Stormwater Detention/Enhanced WetlandPre-Design Alternative Evaluation Hydrology & Hydraulics, Kent Lagoon Design. February1990. Prepared for City of Kent.
Northwest Hydraulic Consultants Inc., Combined Stormwater Detention/Enhanced WetlandPre-Design Definition of Alternatives, Hydrology & Hydraulics. December 1989. Prepared forCity of Kent.
Northwest Hydraulic Consultants Inc., Combined Stormwater Detention/Enhanced WetlandPre-Design Definition of Alternatives Hydrology & Hydraulics. December 1989. Prepared forCity of Kent.
Northwest Hydraulic Consultants Inc., Combined Stormwater Detention/Enhanced WetlandPre-Design Review of Background Materials Hydrology & Hydraulics. August 1989. Preparedfor City of Kent.
Northwest Hydraulic Consultants Inc., Analysis of Proposed Stormwater Control at the KentSewage Treatment Lagoons, Phase III Detention Pond Performance. March 1989. Prepared forCity of Kent.
Northwest Hydraulic Consultants Inc., Analysis of Proposed Stormwater Control at the KentSewage Treatment Lagoons, Phase II Hydrological Modeling. January 1989. Prepared for Cityof Kent.
Northwest Hydraulic Consultants, Inc., Scriber Creek Flood Plain Mapping Study. July 1990. Prepared for City of Lynnwood, Department of Public Works.
Northwest Hydraulic Consultants Inc., Mount Vernon Study Main Stem Channel Assessmentfor the Comprehensive Surface Water Management Plan. October 1991. Subconsultants to R.W. Beck. Prepared for the City of Mount Vernon Engineering.
Northwest Hydraulic Consultants, Inc., Scriber Creek Watershed Management Plan. December 1989. As subconsultants to R. W. Beck. Prepared for Snohomish County
Public Works Surface Water Management, City of Lynnwood Department of Public
12
Works, and City of Brier Department of Public Works.
Northwest Hydraulic Consultants Inc., Normandy Park HSPF Modeling for the NormandyPark Area Flood Control Management Plan. February 1992. As subconsultants to R. W. Beck. Prepared for the City of Normandy Park.
Northwest Hydraulic Consultants Inc., Eastside Green River Watershed Hydrologic Analysis. December 1991. Prepared for R. W. Beck & Associates and City of Renton Department ofPublic Works.
Northwest Hydraulic Consultants Inc., Mill Creek (Auburn) HSPF Model Calibration. July1993. Prepared for King County Division of Surface Water Management, City of Auburn, Cityof Kent.
Northwest Hydraulic Consultants Inc., Preliminary Draft Report on Hydrologic Modeling forCedar Hills Landfill. May 1993. Prepared for King County Office of Prosecuting Attorney,King County Courthouse, Seattle, 98104.
Northwest Hydraulic Consultants Inc., Miller Creek Regional Stormwater Detention FacilitiesDesign Hydrologic Modeling. November 1990. Prepared for King County Division of SurfaceWater Management.
Northwest Hydraulic Consultants, Inc., Issaquah Creek and Tibbetts Creek Basins HSPF ModelCalibration. October 1990. Prepared for King County Division of Surface Water Management,7th Floor Dexter Horton Building, Seattle, 98104.
Northwest Hydraulic Consultants, Inc., Scriber Creek Watershed Management Plan. December 1989. As subconsultants to R. W. Beck. Prepared for Snohomish County PublicWorks Surface Water Management, City of Lynnwood Department of Public Works, and Cityof Brier Department of Public Works.
Northwest Hydraulic Consultants Ltd., Land use and Hydrology, Hydrologic SimulationModeling (HSPF) Salmon River Basin, Lower Fraser Valley, Phase I, Final Report. March 1994. Prepared for Department of Fisheries and Oceans, Fraser River Action Plan, 555 W. HastingsStreet, Vancouver, B.C., V6B 5G2.
Northwest Hydraulic Consultants Ltd., Calibration of the HSPF Hydrologic Simulation ModelUsing Carnation Creek Data. November 1993. Prepared for Forestry Canada, Pacific ForestryCenter, 506 W. Burnside Road, Victoria, B.C., V8Z1M5.
Northwest Hydraulic Consultants Inc., 356th Street Depression - HSPF Hydrologic Modeling. January 17, 1992, Letter Report to Brown & Caldwell Consultants, Seattle, WA 98119.
file:hspfbib.wp5
EPA/600/3-91/039June 1991
Modeling of Nonpoint Source WaterQuality in Urban and Non-urban Areas
by
Anthony S. Donigian, Jr.AQUA TERRA Consultants
Mountain View, California 94093-1004
and
Wayne C. HuberUniversity of Florida
Gainesville, Florida 32611-2013
Contract No. 68-03-3513
Project Technical Monitor
Thomas O. Barnwell, Jr.Assessment Branch
Environmental Research LaboratoryAthens, Georgia 30613
ENVIRONMENTAL RESEARCH LABORATORYOFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCYATHENS, GA 30613
ii
Disclaimer
The work presented in this document has beenfunded by the U. S. Environmental Protection Agencyunder Contract No. 68-03-3513 to AQUA TERRAConsultants. It has been subjected to the Agency'speer and administrative review and has beenapproved as an EPA document. Mention of tradenames or commercial products does not constituteendorsement or recommendation for use.
iii
Foreword
As environmental controls become more costly toimplement and the penalties of judgment errorsbecome more severe, environmental qualitymanagement requires more efficient analytical toolsbased on greater knowledge of the environmentalphenomena to be managed. As part of thisLaboratory's research on the occurrence, movement,transformation, impact, and control of environmentalcontaminants, the Assessment Branch developsmanagement and engineering tools to help pollutioncontrol officials address environmental problems.
Pollutants in runoff and seepage from urban,agricultural, and forested areas contributesignificantly to water pollution problems in manyareas of the United States. The development andapplication of computer-operated mathematicalmodels to simulate the movement of pollutants andthus to anticipate environmental problems has beenthe subject of extensive research by governmentagencies, universities, and private companies formany years. This review and model-selectionguidance document was developed under thedirection of EPA's Office of Water and Office ofResearch and Development to assist water qualityplanners in applying modeling techniques to thedevelopment of cost-effective nonpoint sourcecontrols.
Rosemarie C. Russo, Ph.D.DirectorEnvironmental Research
LaboratoryAthens, Georgia
iv
Abstract
Nonpoint source assessment procedures andmodeling techniques are reviewed and discussed forboth urban and non-urban land areas. Detailedreviews of specific methodologies and models arepresented, along with overview discussions focussingon urban methods and models, and on non-urban(primarily agricultural) methods and models. Simpleprocedures, such as constant concentration, regres-sion, statistical, and loading function approaches aredescribed, along with complex models such asSWMM,
HSPF, STORM, CREAMS, SWRRB, and others. Briefcase studies of ongoing and recently completedmodeling efforts are described. Recommendations fornonpoint runoff quality modeling are presented toelucidate expected directions of future modelingefforts. This work was performed as Work Assign-ment No. 29 under EPA Contract No. 68-03-3513 withAQUA TERRA Consultants. Work was complete as ofMarch 1990.
v
Contents
PageDisclaimer......................................................................................................................................................................... ii
Foreword...........................................................................................................................................................................iii
Abstract............................................................................................................................................................................ iv
Acknowledgments ..........................................................................................................................................................vii
Section 1.0 - Nonpoint Source Modeling Objectives and Considerations .................................................................. 1
Section 2.0 - Overview of Nonpoint Source Quality Modeling ................................................................................... 32.1 Modeling Fundamentals.......................................................................................................................... 32.2 Operational Models.................................................................................................................................. 32.3 Surveys and Reviews of Nonpoint Source Models ............................................................................... 32.4 Summary of Data Needs.......................................................................................................................... 4
Section 3.0 - Overview of Available Urban Modeling Options ................................................................................... 53.1 Introduction .............................................................................................................................................. 53.2 Constant Concentration or Unit Loads .................................................................................................. 53.3 Spreadsheets ............................................................................................................................................. 53.4 Statistical Method..................................................................................................................................... 63.5 Regression?Rating Curve Approaches................................................................................................... 73.6 Buildup and Washoff ............................................................................................................................... 73.7 Related Mechanisms ................................................................................................................................ 8
Section 4.0 - Overview of Available Non-urban Modeling Options .......................................................................... 104.1 Nonpoint Source Loading Functions..................................................................................................... 104.2 Simulation Models .................................................................................................................................. 12
Section 5.0 - Nonpoint Source Runoff Quality Simulation Models and Methods .................................................... 185.1 Urban Runoff Quality Models................................................................................................................ 185.2 Non-urban Runoff Quality Models and Methods ................................................................................ 195.3 Discussion................................................................................................................................................ 22
Section 6.0 - Brief Case Studies ..................................................................................................................................... 266.1 Urban Model Applications ..................................................................................................................... 266.2 Non-urban Model Applications ............................................................................................................. 27
Section 7.0 - Summary and Recommendations for Nonpoint Source Runoff Quality Modeling............................ 29
Section 8.0 - References.................................................................................................................................................. 31
Section 9.0 - Appendix: Detailed Model Descriptions ................................................................................................ 37Storage, Treatment, Overflow, Runoff Model (STORM) ................................................................................ 43Distributed Routing Rainfall Runoff Model?Quality DR3M-QUAL............................................................. 46EPA Screening Procedures ................................................................................................................................ 49Agricultural NonPoint Source Pollution Model? (AGNPS)............................................................................ 52Areal Nonpoint Source Watershed Environment Response Simulation? (ANSWERS)................................ 55Chemicals, Runoff, and Erosion from Agricultural Management Systems? (CREAMS) ............................. 57Groundwater Loading Effects of Agricultural Management Systems? (GLEAMS) ..................................... 57
vi
Hydrological Simulation Program?Fortran (HSPF)........................................................................................ 60Stream Transport and Agricultural Runoff of Pesticides for Exposure Assessment (STREAM) ................ 60Pesticide Root Zone Model?PRZM.................................................................................................................. 64Simulator for Water Resources in Rural Basins? (SWRRB)............................................................................. 67Pesticide Runoff Simulator? (PRS) .................................................................................................................... 67Unified Transport Model for Toxic Materials ?UTM-TOX.............................................................................. 70
vii
Acknowledgments
This nonpoint model review was sponsored by theEPA Office of Water Regulations and Standards,through the Environmental Research Laboratory inAthens, Georgia. Mr. Thomas O. Barnwell was theEPA Project Technical Monitor, with the AssessmentBranch in Athens, and Dr. Hiranmay Biswas was theliaison with EPA Headquarters. Their support andguidance for this effort is gratefully acknowledged.
Dr. Wayne Huber of the University of Floridaperformed the urban model review, and Mr. AnthonyDonigian of AQUA TERRA Consultants performedthe review of the non-urban models. Both authorsreviewed the entire report. Mr. Avinash Patwardhanassisted Mr. Donigian with the information gatheringand review of the non-urban models. Also, the draftfinal report was peer reviewed by Dr. VladimirNovotny of Milwaukee, Wisconsin; and Dr. Matt C.Smith of the University of Georgia, Athens, Georgia.Their insightful comments and suggestions weresincerely appreciated.
viii
Section 1.0
Nonpoint Source Modeling Objectives and Considerations
Studies and projects involving stormwater runoffquality from all categories of land use?urban,agricultural cropland, pasture, forest? can relate tomany environmental problems. In the broadest sense,water quality studies may be performed to protect theenvironment under various state and federallegislation. For example, Section 304(l) of the CleanWater Act requires States to identify waterbodiesimpaired by both point and nonpoint source pollutionand develop appropriate control strategies; whileSection 405 will eventually require analysis ofstormwater outfalls in all urban areas in the U.S. In anarrower sense, a study may address a particularwater quality issue in a particular receiving water,such as bacterial contamination of a beach, release ofoxygen demanding material into a stream or river,unacceptable aesthetics of an open channel receivingurban and non-urban runoff, eutrophication of a lake,contamination of basements from surcharged sewersdue to wet-weather flooding, etc.
By no means should it be assumed that every waterquality problem requires a water quality modelingeffort. Some problems may be mostly hydraulic innature, e.g., the basement flooding problem. That is,the solution may often reside primarily in ahydrologic or hydraulic analysis in which theconcentration or load of pollutants is irrelevant. Insome instances, local or state regulations mayprescribe a nominal ? solution? without recourse toany water quality analysis as such. For example,stormwater runoff in Florida is considered? controlled? through retention or detention withfiltration of the runoff from the first inch of rainfall forareas of 100 acres or less. Other problems may beresolved through the use of measured data withoutthe need to model. In other words, many problems donot require water quality modeling at all.
If a problem does require modeling, specific modelingobjectives will need to be defined to guide themodeling exercise and approach. Models may be used
for objectives such as the following:
1. Characterize runoff quantity and quality as totemporal and spatial detail, concentration/loadranges, etc.
2. Provide input to a receiving water quality analysis,e.g., drive a receiving water quality model.
3. Determine effects, magnitudes, locations,combinations, etc. of control options.
4. Perform frequency analysis on quality parameters,e.g., to determine return periods ofconcentrations/loads.
5. Provide input to cost-benefit analyses.
Objectives 1 and 2 characterize the magnitude of theproblem, and objectives 2 through 5 are related to theanalysis and solution of the problem. Computermodels allow some types of analysis, such asfrequency analysis, to be performed that could rarelybe performed otherwise since periods of water qualitymeasurements are seldom very long. It should alwaysbe borne in mind, however, that use of measured datais usually preferable to use of simulated data,particularly for objectives 1 and 2 in which accurateconcentration values are needed. In general, modelsare not good substitutes for good field samplingprograms. On the other hand, models can sometimesbe used to extend and extrapolate measured data.
Careful consideration should be given to objectivenumber 2. The first urban runoff quality model(SWMM) inadvertently overemphasized the conceptof simulation of detailed intra-storm qualityvariations, e.g., production of a ?pollutograph?(concentration vs. time) at 5 or 10 minute intervalsduring a storm for input to a receiving water qualitymodel. The early agricultural runoff quality models(e.g., ARM and ACTMO models) followed a similar
2
detailed approach primarily to evaluate anddemonstrate the models' abilities to representobserved data from small (less than 10 hectare)monitored fields.
But the fact is that the quality response of mostreceiving waters is insensitive to such short-termvariations, as illustrated in Table 1. In most instances,the total storm load will suffice to determine thereceiving water response, eliminating the necessity ofbecoming embroiled in calibration against detailedpollutographs especially for conventional pollutants.Instead, only the total storm loads need be matched, amuch easier task. Exceptions to this generalobservation may be appropriate when consideringtoxic pollutants, e.g., pesticides, when short-termconcentrations may be lethal to aquatic organisms.Also, simulation of short time increment changes inconcentrations and loads is generally necessary foranalysis of control options, such as storage orhigh-rate treatment, whose efficiency may depend onthe transient behavior of the quality constituents.
Any consideration of water quality modeling meansthat some additional data will be required for modelinput. As described later, such requirements may beas simple as a constant concentration, or much morecomplex such as soil nitrification or mineralizationrates. Data may be obtained from existing studies ortheir acquisition may require
extensive field monitoring. For someconceptualizations of the urban quality cycle, e.g.,buildup and washoff, it may not be routinely possibleto physically measure fundamental input parameters,and such parameters will only be obtained throughmodel calibration. Involvement in acquisition ofquality data, be it through literature reviews or fieldsurveys, profoundly escalates the level of effortrequired for the study. Details on data requirementsfor modeling will be deferred until modelingtechniques are described.
Table 1. Required temporal detail for receiving water analysis
(After Driscoll, 1979, and Hydroscience, 1979)
Type of Key ResponseReceiving Water Constituents Time
Lakes, Bays Nutrients Weeks?Years
Estuaries Nutrients, OD* Days?WeeksBacteria
Large Rivers OD, Nitrogen Days
Streams OD, Nitrogen Hours?DaysBacteria Hours
Ponds OD, Nutrients Hours?Weeks
Beaches Bacteria Hours
*OD = oxygen demand, e.g., BOD, that affects dissolved oxygen.
3
Section 2.0
Overview of Nonpoint Source Quality Modeling
2.1 Modeling Fundamentals
Modeling caveats and an introduction to modeling arepresented by several authors including James andBurges (1982), Kibler (1982), Huber (1985, 1986) andsummarized in a recent manual of practice (WPCF,1989). Space does not permit a full presentation here;a few items are highlighted below.
1. Have a clear statement of project objectives.Verify the need for quality modeling. (Perhapsthe objectives can be satisfied without qualitymodeling).
2. Use the simplest model that will satisfy theproject objectives. Often a screening model, e.g.,regression or statistical, can determine whethermore complex simulation models are needed.
3. To the extent possible, utilize a qualityprediction method consistent with availabledata. This would ordinarily rule againstbuildup-washoff formulations, although thesemight still be useful for detailed simulation,especially if calibration data exist.
4. Only predict the quality parameters of interestand only over a suitable time scale. That is,storm event loads and EMCs will usually be themost detailed prediction necessary, and seasonalor annual loads will sometimes be all that isrequired. Do not attempt to simulate intra-stormvariations in quality unless it is necessary.
5. Perform a sensitivity analysis on the selectedmodel and familiarize yourself with the modelcharacteristics.
6. If possible, calibrate and verify the modelresults. Use one set of data for calibration and
another independent set for verification. If nosuch data exist for the application site, perhapsthey exist for a similar catchment nearby.
2.2 Operational Models
Implementation of an off-the-shelf model or methodwill be easiest if the model can be characterized as?operational? in the sense of:
1. Documentation. This should include a user'smanual, explanation of theory and numericalprocedures, data needs, data input format, etc.Documentation most often separates the manycomputerized procedures found in the literaturefrom a model that can be accessed and easilyused by others.
2. Support. This is sometimes provided by themodel developer but often by a federal agencysuch as the HEC or EPA.
3. Experience. Every model must be used a ? firsttime? but it is best to rely on a model with aproven track record.
The models described in Sections 3.0 and 4.0 are alloperational in this sense. New methods and modelsare constantly under development and should not beneglected simply because they lack one of thesecharacteristics, but the user should be aware ofpotential difficulties if any characteristic is lacking.
2.3 Surveys and Reviews of Nonpoint
Source Models
4
Several publications, often somewhat out of date,provide reviews of available models. Some models(e.g., SWMM, STORM, HSPF, CREAMS) havepersisted for many years and are included in botholder and newer reviews, while other models (e.g.,USGS, Statistical, spreadsheet, AGNPS, SWRRB) aremore recent. Reviews that consider surface runoffquality models include Huber and Heaney (1982),Kibler (1982), Whipple et al. (1983), Barnwell (1984,1987), Huber (1985, 1986), Donigian and Beyerlein(1985), Bedient and Huber (1988), and Viessman et al.(1989). HEC models are described in detail byFeldman (1981). Descriptions of EPA nonpoint sourcewater quality models are provided by Ambrose et al.(1988) and Ambrose and Barnwell (1989). Selectedpesticide runoff models have been reviewed byMulkey et al. (1986) and Lorber and Mulkey (1982).Agricultural nonpoint source models have been show-cased in a number of conferences and symposia overthe past few years, including a 1983 Symposium onNatural Resources Modeling (DeCoursey, 1985); a1984 Conference on Agricultural Nonpoint sourcePollution: Model Selection and Application, in Venice,Italy (Giorgini and Zingales, 1986); and a June 1988International Symposium on Water Quality Modelingof Agricultural Nonpoint Sources (DeCoursey, InPress). Beasley and Thomas (1989) describe recentmodel enhancements and applications for fiveselected models to agricultural and forestedwatersheds in the southeastern U.S. These reports andproceedings provide a wealth of information oncurrent efforts and recent developments in modelingnonpoint contributions and water quality impacts ofagricultural activities.
2.4 Summary of Data Needs
In application of most models, there are twofundamental types of data requirements. First, thereare the data needed simply to make the modelfunction, that is, input parameters and timeseries datafor the model. These typically include precipitation(rainfall) and other meteorologic informa-tion, drainage area, imperviousness, runoff coefficientand other quantity prediction parameters, plusquality prediction parameters such as constantconcentration, constituent median and CV, regressionrelationships, buildup and washoff parameters,soil/chemical characteristics, partition coefficients,reaction rates, etc. In other words, each mode
will have a fundamental list of required input data.Although it is difficult to generalize for the entireuniverse of both simple and complex nonpoint sourcemodels, Novotny and Chesters (1981) have prepared asummary table of required input data from whichTable 2 was adapted.
The second type of information is required forcalibration and verification of more complex models,namely, sets of measured runoff and quality samples(coincident with the input precipitation andmeteorologic data) with which to test the model. Suchdata exist (e.g., Huber et al., 1982; Driver et al. (1985),Noel et al., 1987) but seldom for the site of interest. Ifthe project objectives absolutely require such data(e.g., if a model must be calibrated in order to drive areceiving water quality model), then expensive localmonitoring may be necessary.
This summary relates primarily to quality predictionand may not represent a comprehensive statement ofdata needs for quantity prediction. However, sincerainfall and runoff are required for virtually everystudy, certain quantity-related parameters are alsonecessary for various methods.
5
Table 2. Input data needs for nonpoint source models
(after Novotny and Chesters, 1981)
1. System Parametersa. Watershed Sizeb. Subdivision of the Watershed into Homogenous Subareasc. Imperviousness of Each Subaread. Slopese. Fraction of Impervious Areas Directly Connected to a Channelf. Maximum Surface Storage (depression plus interception storage)g. Soil Characteristics Including Texture, Permeability, Erodibility, and Compositionh. Crop and Vegetation Coveri. Curb Density or Street Gutter Lengthk. Sewer System or Natural Drainage Characteristics
2. State Variablesa. Ambient Temperatureb. Reaction Rate Coefficientsc. Adsorption/Desorption Coefficientsd. Growth Stage of Cropse. Daily Accumulation Rates of Litterf. Traffic Density and Speedg. Potency Factors for Pollutants (pollutant strength on sediment)h. Solar Radiation (for some models)
3. Input Variablesa. Precipitationb. Atmospheric Falloutc. Evaporation Rates
6
Section 3.0
Overview of Available Urban Modeling Options
3.1 Introduction
Several quality modeling options exist for simulationof quality in urban storm and combined sewersystems. These have been reviewed by Huber (1985;1986) and range from simple to involved, althoughsome ? simple? methods, e.g., the EPA statisticalmethods, can incorporate quite sophisticatedconcepts. The principal methods available to thecontemporary engineer are outlined below, in a roughorder of complexity. Their data requirements aresummarized in Table 3. The methods are:
1. Constant concentration
2. Spreadsheet
3. Statistical
4. Rating curve or regression
5. Buildup/washoff
3.2 Constant Concentration or Unit Loads
As its name implies, all runoff is assumed to have thesame, constant concentration for a given pollutant. Atits very simplest, an annual runoff volume can bemultiplied by a concentration to produce an annualrunoff load. However, this option may be coupledwith a hydrologic model, wherein loads (product ofconcentration and flow) will vary if the modelproduces variable flows. This option may be quiteuseful because it may be used with any hydrologic orhydraulic model to produce loads, merely bymultiplying by the constant concentration. Forinstance, the highly sophisticated SWMM ExtranBlock may be used for hydraulic analysis of sewer
system, prediction of overflows and diversions toreceiving waters, etc., yet it performs no quality sim-ulation as such. In many instances, it may be most im-portant to get the volume and timing of suchoverflows and diversions correctly, and simplyestimate loads by multiplying by a concentration.
An obvious question is what (constant) concentrationto use? The EPA NURP studies (EPA, 1983) haveproduced a large and invaluable data base fromwhich to select numbers, but the 30 city coverage ofNURP will most often not include a siterepresentative of the area under study. Nonetheless, alarge data base does exist from which to review con-centrations. Another option is to use measured valuesfrom the study area. This might be done from alimited sampling program. However, the NURP studyconclusively demonstrated the variation that exists inevent mean concentrations (EMCs, total storm eventload divided by total storm event runoff volume) at asite, within a city, and within a region or the countryas a whole. Thus, while use of a constantconcentration may produce load variations, EMCvariations will not be replicated. These variations maybe important in the study of control options andreceiving water responses.
Unit loads are perhaps an even simpler concept.These consist of values of mass per area per time,typically lb/ac-yr or kg/ha-yr, for various pollutants,although other normalizations such as lb/curb-mileare sometimes encountered. Annual (or other timeunit) loads are thus produced upon multiplication bythe contributing area. Such loadings are obviouslyhighly site-specific and depend upon both demo-graphic and hydrologic factors. They must be basedon an average or ? typical? runoff volume and cannotvary from year to year, but they can conveniently besubject to reduction by best management practices(BMPs), if the BMP effect is known. Although earlyEPA references provide some information for various
7
land used (EPA, 1973; EPA, 1976a; McElroy et al.,1976), unit loading rates are exceedingly variable anddifficult to transpose from one area to another.Constant concentrations can sometimes be used forthis purpose, since mg/1 x 0.2265 = lb/ac per inch ofrunoff. Thus, if a concentration estimate is available,the annual loading rate, for example, may becalculated by multiplying by the inches per year ofrunoff. Finally, the Universal Soil Loss Equation(Wischmeier and Smith, 1978; Heaney et al., 1975) wasdeveloped to estimate tons per acre per year of sedi-ment loss from land surfaces. If a pollutant may beconsidered as a fraction (?potency factor? ) ofsuspended solids concentration or load, this offersanother option for prediction of annual loads. Lageret al. (1977), Manning et al. (1977) and Zison (1980)provide summaries of such values.
3.3 Spreadsheets
Microcomputer spreadsheet software, e.g., Lotus,Quattro, Excel, is now ubiquitous in engineeringpractice. Very extensive and highly sophisticatedengineering analysis is routinely implemented onspreadsheets, and water quality simulation is noexception. In essence, the spreadsheet may be used toautomate and extend the concept of the constantconcentration idea. In the usual manifestation of thisspreadsheet application, runoff volumes arecalculated very simplistically, usually using a runoffcoefficient times a rainfall depth. The coefficient mayvary according to land use, or an SCS procedure maybe used, but the hydrology is inherently simplistic inthe spreadsheet predictions. The runoff volume isthen multiplied by a constant concentration to predictrunoff loads. The advantage of the spreadsheet is thata mixture of land uses (with varying concentrations)may easily be simulated, and an overall load andflow-weighted concentration obtained from the studyarea (Walker et al., 1989). The study area itself mayrange from a single catchment to an entire urban area.The relative contributions of different land uses maybe easily identified, and handy spreadsheet graphicstools used for display of the results.
As an enhancement, control options may be simulatedby application of a constant removal fraction for anassumed BMP. Although spreadsheet computationscan be amazingly complex, BMP simulation is rarelymore complicated than a simple removal fractionbecause anything further would require simulation of
the dynamics of the removal device (e.g., a wetdetention pond), which is usually beyond the scope ofthe hydrologic component of the spreadsheet model.Nonetheless, if simple BMP removal fractions can bebelieved, the spreadsheet can easily be used toestimate the effectiveness of control options. Loadswith and without controls can be estimated andproblem areas, by contributing basin and land use,can be determined. Since most engineers are familiarwith spreadsheets, such models can be developedin-house in a logical manner.
The spreadsheet approach is best suited to estimationof long-term loads, such as annual or seasonal,because very simple prediction methods generallyperform better over a long averaging time and poorlyat the level of a single storm event. Hence, althoughthe spreadsheet could be used at the microscale (at orwithin a storm event) it is most often applied formuch longer time periods. It is harder to obtain thevariation of predicted loads and concentrations usingthe spreadsheet method because this can ordinarilyonly be done by varying the input concentrations orrainfall values. A Monte Carlo simulation may beattempted (i.e., systematic variation of all inputparameters according to an assumed frequencydistribution) if the number of such parameters is nottoo large. These results may then be used to estimatethe range and/or frequency distribution of predictedloads and concentrations.
In a generic sense, the spreadsheet idea may be usedin methods programmed in other languages, e.g.,Fortran. For example, comprehensive assessments ofcoastal zone pollution from urban areas are made byNOAA (1987) by assembling land use data withdifferent runoff coefficients, predicting daily andseasonal runoff volumes from daily rainfall, andpredicting seasonal pollutant loads using constantconcentrations. Although the demographic data baseand use of magnetic tapes may dictate use ofmainframes, the computational concept is still that ofa spreadsheet.
Again, the question arises of what concentrations touse, this time potentially for multiple land uses andsubareas. And again, the NURP data base will usuallybe the first one to turn to, with the possibility of localmonitoring to augment it.
8
3.4 Statistical Method
The so-called ?EPA Statistical Method? is somewhatgeneric and until recently was not implemented inany off-the-shelf model or even very well in any singlereport (Hydroscience, 1979; EPA, 1983). A new FHWAstudy (Driscoll et al., 1989) partially remedies thissituation. The concept is straightforward, namely thatof a derived frequency distribution for EMCs. Thisidea has been used extensively for urban runoffquantity (e.g., Howard, 1976; Loganathan andDelleur, 1984; Zukovs et al., 1986) but not as much forquality predictions.
The EPA Statistical Method utilizes the fact that EMCsare not constant but tend to exhibit a lognormalfrequency distribution. When coupled with anassumed distribution of runoff volumes (alsolognormal), the distribution of runoff loads may bederived. When coupled again to the distribution ofstreamflow, an approximate (lognormal) probabilitydistribution of in-stream concentrations may bederived (Di Toro, 1984)?a very useful result, althoughassumptions and limitations of the method have beenpointed out by Novotny (1985) and Roesner andDendrou (1985). Further analytical methods have beendeveloped to account for storage and treatment (DiToro and Small, 1979; Small and Di Toro, 1979). Themethod was used as the primary screening tool in theEPA NURP studies (EPA, 1983) and has also beenadapted to combined sewer overflows (Driscoll, 1981)and highway-related runoff (Driscoll et al., 1989). Thislatter publication is one of the best for aconcise explanation of the procedure and assumptionsand includes spreadsheet software for easyimplementation of the method.
A primary assumption is that EMCs are distributedlognormally at a site and across a selection of sites.The concentrations may thus be characterized by theirmedian value and by their coefficient of variation (CV= standard deviation divided by the mean). There islittle doubt that the lognormality assumption is good(Driscoll, 1986), but similar to the spreadsheetapproach, the method is then usually combined withweak hydrologic assumptions, e.g., prediction ofrunoff using a runoff coefficient. (The accuracy of arunoff coefficient increases as urbanization and imper-viousness increase.) However, since many streams ofconcern in an urban area consist primarily ofstormwater runoff during wet weather, the ability topredict the distribution of EMCs is very useful for
assessment of levels of exceedance of water qualitystandards. The effect of BMPs can again be estimatedcrudely through constant removal fractions that effecton the coefficient of variation. Overall, the methodhas been very successfully applied as a screening tool.
Input to the method as implemented for the FHWA(Driscoll et al., 1989) includes statistical properties ofrainfall (mean and coefficient of variation of stormevent depth, duration, intensity and interevent time),area, and runoff coefficient for the hydrologiccomponent, plus EMC median and coefficient ofvariation for the pollutant. Generalized rainfallstatistics have already been calculated for manylocations in the U.S. Otherwise, the EPA SYNOPmodel (EPA, 1976b; Hydroscience, 1979; EPA, 1983;Woodward-Clyde, 1989) must be run on long-termhourly rainfall records. If receiving water impact is tobe evaluated the mean and CV of the streamflow arerequired plus the upstream concentration. AVollenweider-type lake impact analysis is alsoprovided based on phosphorus loadings.
As with the first two methods discussed, the choice ofmedian concentration may be difficult, and theStatistical Method requires a coefficient of variation aswell. Fortunately, from NURP and highway studies,CV values for most urban runoff pollutants are fairlyconsistent, and a value of 0.75 is typical. If localand/or NURP data are not available or inappropriate,local monitoring may be required, as in virtuallyevery quality prediction method. The estimation ofthe whole EMC frequency distribution for a pollutantis a definite advantage of the Statistical Method oversome applications of constant concentration andsimple spreadsheet approaches. Frequency analyses ofwater quantity and quality parameters may also beperformed on the output of continuous simulationmodels such as HSPF, SWMM and STORM. Thederived distribution approach of the StatisticalMethod avoids the considerable effort required forcontinuous simulation at the expense of simplifyingassumptions that may or may not reflect theprototype situation adequately.
3.5 Regression?Rating Curve Approaches
With the completion of the NURP studies in 1983,there are measurements of rainfall, runoff and waterquality at well over 100 sites in over 30 cities. Someregression analysis has been performed to try to relate
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loads and EMCs to catchment, demographic andhydrologic characteristics (e.g., McElroy et al., 1976;Miller et al., 1978; Brown, 1984), the best of which arerecent results of the USGS (Tasker and Driver, 1988;Driver and Tasker, 1988), to be described brieflybelow. Regression approaches have also been used toestimate dry-weather pollutant deposition in com-bined sewers (Pisano and Queiroz, 1977), a task atwhich no model is very successful. What are termed? rating curves? herein are just a special form ofregression analysis, in which concentration and/orloads are related to flow rates and/or volumes. This isan obvious exercise attempted at most monitoringsites and has a historical basis in sediment dischargerating curves developed as a function of flow rate innatural river channels.
A rating curve approach is most often performedusing total storm event load and runoff volumealthough intra-storm variations can sometimes besimulated in this manner as well (e.g., Huber andDickinson, 1988). It is usually observed (Huber, 1980;EPA, 1983; Driscoll et al., 1989) that concentration(EMC) is poorly or not correlated with runoff flow orvolume, implying that a constant concentrationassumption is adequate. Since the load is the productof concentration and flow, load is usually wellcorrelated with flow regardless of whether or notconcentration correlates well. Manifestation ofspurious correlation (Benson, 1962) is often ignored inurban runoff studies. If load is proportional to flow tothe first power (i.e., linear), then the constantconcentration assumption holds; if not, somerelationship of concentration with flow is implied.Rating curve results can be used by themselves forload and EMC estimates and can be incorporated intosome models (e.g., SWMM, HSPF).
Rainfall, runoff and quality data were assembled for98 urban stations in 30 cities (NURP and other) in theU.S. for multiple regression analysis by the USGS(Driver and Tasker, 1988; Tasker and Driver 1988).Thirty-four multiple regression models (mostlylog-linear) of storm runoff constituent loads andstorm runoff volumes were developed, and 31 modelsof storm runoff EMCs were developed. Regional andseasonal effects were also considered. The two mostsignificant explanatory variables were total stormrainfall and total contributing drainage area.Impervious area, land use, and mean annual climaticcharacteristics also were significant explanatoryvariables in some of the models. Models forestimating loads of dissolved solids, total nitrogen,
and total ammonia plus organic nitrogen (TKN)generally were the most accurate, whereas models forsuspended solids were the least accurate. The mostaccurate models were those for the more arid WesternU.S., and the least accurate models were those forareas that had large mean annual rainfall.
These USGS equations represent the best generalizedregression equations currently available for urbanrunoff quality prediction. Note that such equations donot require preliminary estimates of EMCs or localquality monitoring data except for the very usefulexercise of verification of the regression predictions.Regression equations only predict the mean and donot provide the frequency distribution of predictedvariable, a disadvantage compared to the statisticalapproach. (The USGS documentation describesprocedures for calculation of statistical error bounds,however). Finally, regression approaches, includingrating curves, are notoriously difficult to applybeyond the original data set from which therelationships were derived. That is, they are subject tovery large potential errors when used to extrapolateto different conditions. Thus, the usual caveats aboutuse of regression relationships continue to hold whenapplied to prediction of urban runoff quality.
3.6 Buildup and Washoff
In the late 1960s, a Chicago study by the AmericanPublic Works Association (1969) demonstrated the(assumed linear) buildup of ?dust and dirt? andassociated pollutants on urban street surfaces. Duringa similar time frame, Sartor and Boyd (1972) alsodemonstrated buildup mechanisms on the surface aswell as an exponential washoff of pollutants duringrainfall events. These concepts were incorporated intothe original SWMM model (Metcalf and Eddy et al.,1971) as well as into the STORM, USGS and HSPFmodels to a greater or lesser degree (Huber, 1985).?Buildup? is a term that represents all of the complexspectrum of dry-weather processes that occurbetween storms, including deposition, wind erosion,street cleaning, etc. The idea is simply that all suchprocesses lead to an accumulation of solids andperhaps other pollutants that are then ?washed off?during storm events.
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Although ostensibly physically based, models thatinclude buildup and washoff mechanisms reallyemploy conceptual algorithms because the truephysics is related to principles of sediment transportand erosion that are poorly understood in thisframework. Furthermore, the inherent heterogeneityof urban surfaces leads to use of average buildup andwashoff parameters that may vary significantly fromwhat may occur in an isolated street gutter, forexample. Thus, except in rare instances ofmeasurements of accumulations of surface solids, theuse of buildup and washoff formulations inevitablyresults in a calibration exercise against measuredend-of-pipe quality data. It then holds that in theabsence of such data, inaccurate predictions can beexpected.
Different models offer different options for conceptualbuildup and washoff mechanisms, with SWMMhaving the greatest flexibility. In fact, with calibration,good agreement can be produced between predictedand measured concentrations and loads with suchmodels, including intra-storm variations that cannotbe duplicated with most of the methods discussedearlier. (When a rating curve is used in SWMMinstead of buildup and washoff, it is also possible tosimulate intra-storm variations in concentration andload.) A survey of linear buildup rates for manypollutants by Manning et al. (1977) is probably thebest source of generalized buildup data, and someinformation is available in the literature to aid inselection of washoff coefficients (Huber 1985; Huberand Dickinson, 1988). However, such first estimatesmay not even get the user in the ball park (i.e.,quality?not quantity?predictions may be off by morethan an order of magnitude); the only way to be sureis to use local monitoring data for calibration andverification. Thus, as for most of the other qualityprediction options discussed herein, thebuildup-washoff model may provide adequate com-parisons of control measures, ranking of loads, etc.but cannot be used for prediction of absolute values ofconcentrations and loads, e.g., to drive a receivingwater quality model, without adequate calibrationand verification data. Since buildup and washoff aresomewhat appealing conceptually, it is somewhateasier to simulate potential control measures such asstreet cleaning and surface infiltration using thesemechanisms than with, say, a constant concentrationor rating curve method. In the relatively unusualinstance in which intra-storm variations in con-centration and load must be simulated, as opposed tototal storm event EMC or load, buildup and washoff
also offer the most flexibility. This is sometimesimportant for the design of storage facilities in whichfirst-flush mechanisms may be influential.
As mentioned above, generalized data for buildupand washoff are sparse (Manning et al., 1977) andsuch measurements almost never conducted as part ofa routine monitoring program. For buildup,normalized loadings, e.g., mass/day-area ormass/day per curb-length, or just mass/day, arerequired, along with an assumed functional form forbuildup vs. time, e.g., linear, exponential,Michaelis-Menton, etc. For washoff, the relationshipof washoff (mass/time) vs. runoff rate must beassumed, usually in the form of a power equation.When end-of-pipe concentration and load data are allthat are available, all buildup and washoff coefficientsend up being calibration parameters.
3.7 Related Mechanisms
In the discussion above, washoff is assumedproportional to the runoff rate, as for sedimenttransport. Erosion from pervious areas may instead beproportional to the rainfall rate. HSPF does the bestjob of including this mechanism in its algorithms forerosion of sediment from pervious areas. SWMMincludes a weaker algorithm based on the UniversalSoil Loss Equation.
Many pollutants, particularly metals and organics, areadsorbed onto solid particles and are transported inparticulate form. The ability of a model to include?potency factors? (HSPF) or ?pollutant fractions?(SWMM) enhance the ability to estimate theconcentration or load of one constituent as a fractionof that of another constituent, e.g., solids (Zison,1980).
The groundwater contribution to flow in urban areascan be important in areas with unlined and openchannel drainage. Of the urban models discussed,HSPF far and away has the most complexmechanisms for simulation of subsurface waterquality processes in both the saturated andunsaturated zones. Although SWMM includessubsurface flow routing, the quality of subsurfacewater can only be approximated using a constantconcentration.
The precipitation load may be input in some models
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(SWMM, HSPF), usually as a constant concentration.Point source and dry-weather flow (baseflow) loadsand concentrations can also be input to SWMM,STORM and HSPF to simulate backgroundconditions. Other quality sources of potentialimportance include catchbasins (SWMM) andsnowmelt (SWMM, STORM, HSPF).
Scour and deposition within the sewer system can bevery important in combined sewer systems and someseparate storm sewer systems. The state of the art insimulation of such processes is poor (Huber, 1985).SWMM offers a crude but calibratable attempt atsimulation of such processes.
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Table 3. Data needs for various quality prediction methods
Method Data Potential Source
Unit Load Mass per time per unit tributary area. Derive from constant concentration andrunoff. Literature values.
Constant Concentration Runoff prediction mechanism (simple tocomplex).
Existing model; runoff coefficient orsimple method.
Constant concentration for eachconstituent.
NURP; local monitoring.
Spreadsheet Simple runoff prediction mechanism. e.g., runoff coefficient, perhaps asfunction of land use.
Constant concentration or concentrationrange.
NURP; local monitoring.
Removal fractions for controls. NURP; Schueler (1987); local and statepublications.
Statistical Rainfall statistics. NURP; Driscoll et al. (1989); Woodward-Clyde (1989); EPA SYNOP model.
Area, imperviousness. Pollutant medianand CV.
NURP; Driscoll (1986); Driscoll et al.(1989); local monitoring.
Receiving water characteristics andstatistics.
Local or generalized data.
Regression Storm rainfall, area, imperviousness, landuse.
Local data.
Rating Curve Measured flow rates/volumes and qualityEMCs/loads.
NURP; local data.
Buildup Loading rates and rate constants. Literature values*.
Street cleaning removals. Literature values.
Washoff Power relationship with runoff. Literature values.*.
*Usually must be calibrated using end-of-pipe monitored quality data.
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Section 4.0
Overview of Available Non-urban Modeling Options
As with the options for modeling nonpoint sourcepollutants from urban areas, a wide range oftechniques are available for modeling thesecontributions from non-urban land uses, from simpleannual `loading functions' to detailed processsimulation models. The key issue in estimatingnonpoint pollution loads from a watershed, or parcelof land, is the type and extent of human activitiesoccurring (or not occurring) on the land. The samehydrologic, physical, and chemical/biologicalprocesses that determine nonpoint pollutant loadsoccur on all land surfaces (and in the soil profile)whether it is urban, forest, agricultural cropland,pasture, mining, etc. The relative importance andmagnitude of these processes, in determiningnonpoint loads, will vary among land use categoriesand associated human activities. Even within anurban region, the parameters required for the variousmodeling options described in Section 4 will differ forcommercial, industrial, transportation, and variousdensities of residential land. Many of these sameurban modeling options have been used for non-urban land areas with parameters (e.g., constantconcentrations) estimated for the specific non-urbanland use.
The focus of the majority of non-urban nonpointsource estimation procedures and models has been onagricultural cropland, although the procedures andmodels have often been adapted and applied to manyother land use categories. The agricultural researchcommunity, comprised of the U.S. Department ofAgriculture (including the Soil Conservation Serviceand Agricultural Research Service) regional labora-tories and state universities, have developed asignificant body of knowledge of soil processes andprocedures for estimating runoff and soil erosion thathave formed the `building blocks' for loadingfunctions and nonpoint source models. This sectiondiscusses some of the loading function proceduresavailable for agricultural and other non-urban areasand general concepts underlying the more detailed
process simulation models. The individual detailedmodels will be discussed briefly in Section 5.2 withadditional details provided in the Appendix.
4.1 Nonpoint Source Loading Functions
The term `loading function' has been used in thenonpoint pollution literature to describe simplecalculational procedures usually for estimating theaverage annual load, and sometimes the storm eventload, of a pollutant from an individual land usecategory. A number of different loading functionshave been developed and proposed over the past twodecades, the most widely used of which are the EPAScreening Procedures, also referred to as the EPAWater Quality Assessment Methodology. Theseprocedures are described below, followed by a briefdiscussion of a few other loading functions in theliterature.
4.1.1EPA Screening Procedures
The EPA Screening Procedures (Mills et al., 1985; Millset al., 1982) are a revision and expansion of waterquality assessment procedures initially developed fornondesignated 208 areas (Zison et al., 1977). TheProcedures have been expanded and revised toinclude consideration of the accumulation, transport,and fate of toxic chemicals, in addition toconventional pollutants included in the ear-lier versions. The manual includes a separate chapterdescribing calculation procedures for estimatingnonpoint loads for urban and non-urban land areas inaddition to chapters on procedures for rivers andstreams, impoundments, and estuaries. The mostrecent update includes consideration of toxicsloadings and fate/transport in groundwater systems.
The procedures for nonpoint load assessmentsdescribed in the manual are essentially a compilation
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and integration of techniques developed earlier byMidwest Research Institute (MRI) (McElroy et al.,1976), Amy et al. (1974), Heaney et al. (1976) andHaith (1980). However, the presentation of theprocedures is well-integrated, supplemented withadditional parameter estimation guidance, andincludes sample calculations. The procedures for non-urban areas are derived from the MRI loadingfunctions for average annual estimates based on theUniversal Soil Loss Equation (USLE) (Wischmeier andSmith, 1978), while the storm event procedures usethe Modified USLE (Williams, 1975) and the SCSRunoff Curve Number procedure (Mockus, 1972) forstorm runoff volume. Pollutant concentrations inrunoff and soil, and enrichment ratios are required forestimating pollutant loads; precipitation contributionsof nutrients can be included. Specific information isincluded for estimation of salinity loads in irrigationreturn flows. Separate equations are provided forestimating loads for sorbed pollutants, dissolvedpollutants, and partitioned pollutants (i.e., bothsorbed and dissolved phases); this latter category isprimarily for pesticides for which the procedureswere developed by Haith (1980) for storm eventloads. Guidance is provided for estimating allrequired parameters.
The primary strengths and advantages of the EPAScreening Procedures are as follows:
a. Excellent user documentation and guidance,including occasional workshops sponsored bythe EPA Center for Exposure AssessmentModeling, in Athens, GA.
b. No computer requirements since the procedurescan be performed on hand calculators;associated programs have been developed forriver quality analyses (Mills et al., 1979).
c. Loading calculations and procedures can belinked to water quality procedures in otherchapters to assess water quality impacts ofnonpoint source loads.
d. Relatively simple procedures with minimal datarequirements that can be satisfied from the usermanual when site-specific data are lacking.
These screening procedures are well suited for generalscreening-level assessments; however, they sufferfrom the same disadvantages as all such grossestimation techniques. As with the urban options
discussed above, the accuracy of the loads dependson the accuracy of the user-assumed pollutantconcentration; the impacts of management options isusually represented by a simple, constant `removalfraction'; snowmelt and associated loadings are notrepresented; and calculations can be tedious and timeconsuming for complex multi-land use basins. In spiteof these disadvantages and limitations, the EPAScreening Procedures are appropriate for many typesof nonpoint load assessments. They have enjoyedwide popularity, partly due to the availability oftraining workshops sponsored by EPA, and have beenapplied in a number of regions, including theSandusky River in northern Ohio and the Patuxent,Ware, Chester, and Occoquan basins in theChesapeake Bay region (Davis et al., 1981; Dean et al.,1981a; Dean et al., 1981b). Although the proceduresare quite amenable to a computerized or spreadsheetimplementation, to our knowledge no effort has beenmade to implement such a format.
4.1.2Other Loading Functions
As noted earlier, other loading functions have beendeveloped and proposed by various groups andauthors, though none have the support nor have theydemonstrated the longevity of the EPA ScreeningProcedures. The WRENS handbook (Water ResourcesEvaluation of Nonpoint Silvicultural Sources, U.S.Forest Service (1980)) is similar to the EPA proceduresbut its focus is directed to the effects of forestryactivities on water quality. The handbook providesquantitative techniques for estimating potentialchanges in streamflow, surface erosion, soil massmovement, total potential sediment discharge, andwater temperature for comparative analyses ofalternative silvicultural management practices. Runoffand erosion estimation techniques are similar to thoseused in the EPA Screening Procedures withparameters modified for forestry conditions.
Haith and Tubbs (1981) developed watershed loadingfunctions as a screening tool to evaluate agriculturalnonpoint source pollution in large watersheds. Thesefunctions also use the SCS Curve Number procedurefor runofff estimation and the USLE for erosion; then,based on user-defined pollutant concentrations inrunoff and attached to sediment, the procedures allowcalculation of loadings to receiving waters. Thesefunctions have been added to the most recent updateof the EPA Screening Procedures manual (i.e., 1985). A validation of the loading functions for a 850 km2
watershed is described by Haith and Shoemaker
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(1987).
More recently, Li et al. (1989) have proposed loadingfunctions for estimating the average annual pesticideloads in surface runoff. They developed regressionequations derived from 100-year simulations of dailypesticide runoff using the Haith (1980) pesticidemodel. The regression equation coefficients are basedon pesticide half life and soil partition coefficients,and are tabulated in the article for a wide range ofvalues. Two different regressions are described: onebased simply on mean annual soil erosion, and theother based on mean annual soil erosion and surfacerunoff volume during the month of pesticideapplication.
4.1.3Discussion
The loading functions discussed above, and othersimilar techniques in the literature, differ from thesimulation models primarily in time scale definitionand their simplified, mostly empirical techniques forestimating nonpoint loads. They are used primarily toestimate average annual or event loads, and potentialchanges in these loads with land use and managementpractice. These procedures and associated calculationscan usually be performed with a hand calculator, andwith proliferation of personal computers andadvances in computer technology we can expect tosee more and more of these techniques available onPCs. However, users should not interpret the aura ofimplementation on a PC as an improvement in thecapabilities, accuracy, or validity of these techniques.There are significant limitations in these procedures,because of their simplified nature, especially forevaluation of the impacts of management practices.As described above, they often require user-specifiedconcentrations of pollutants in runoff and/or attachedto sediment; some assume the total pollutant load canbe estimated as a function of sediment alone.
Unfortunately, a comprehensive data base,comparable to the NURP data base for urban areas,does not exist for estimating the needed inputconcentrations for the wide range of non-urban landuse categories. Also, there appears to be much greatervariability in runoff concentrations from non-urbanland than from urban land areas; consequently,extrapolation of concentrations from other sites maybe less appropriate for non-urban land categories.Agricultural cropland is especially difficult torepresent by single-valued `representative'concentrations due to differences in crops, fertilizer
applications, tillage practices, agronomic practices,soils characteristics, etc.
In spite of these limitations, loading functions can beuseful for general screening assessments to identifyrelative nonpoint contributions under differentconditions as long as their assumptions andlimitations are recognized. They are more popularthan the detailed simulation models and have thusbeen applied more frequently, primarily because oftheir ease of use. Also, the loading functions are avery useful precursor to more detailed modelingstudies for general problem assessment andidentification and to determine if such detailedstudies are warranted.
4.2 Simulation Models
The primary differences between nonpoint runoffsimulation models and the loading functionsdescribed above relate to the temporal and spatialdetail of the analysis, along with (usually) a morerefined representation of the processes that determinenonpoint pollutant loadings. Whereas the loadingfunctions can be used with only a hand calculator (orspreadsheet), the added detail of most simulationmodels requires a computer code, computer facilities,and significantly more input data, such as dailyrainfall and possibly other meteorologic timeseries.These models are most often computerizedprocedures that perform hydrologic (runoff),sediment erosion, and pollutant (chemical/ biological)calculations on short time intervals, usually rangingfrom one hour to one day, for many years. Theresulting values for each time interval, e.g., runoff,sediment, pollutant load or concentration, can beanalyzed statistically and/or aggregated to daily,monthly, or annual values for estimates of nonpointloadings under the conditions simulated.
As with the urban models, a wide range of nonpointmodels appropriate for non-urban areas are availableand have been used for many different types of landcategories. The available models also cover a largerange of complexity depending on the extent to whichhydrologic, sediment erosion, and chemical/biological processes are modeled in a mechanisticmanner or based on empirical procedures. Similar tourban modeling, many of the same simple proceduresand assumptions used in the loading functions arealso incorporated into a number of simulation
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models, e.g., USLE, SCS Curve Number, constantpollutant concentration. Section 5.2 provides briefsummaries of a number of the more widely used and`operational' non-urban models, along with a briefdiscussion of relative strengths and weaknesses;additional details for each of the models is providedin the Appendix.
In the remainder of this section, we discuss the twomajor modeling efforts that have dominated the non-urban (primarily agricultural) nonpoint modelingarena over the past two decades as a basis fordescribing the types of modeling techniques used fornon-urban land uses. In addition, we will brieflydiscuss the key differences between urban and non-urban models and identify a number of ongoingmodel development efforts.
4.2.1HSPF and CREAMS Model Development
The 1970's and early 1980's was a period of increasing pollution, and corresponding development ofmathematical models to both characterize thepollutant loadings and water quality impacts, andevaluate alternative means of control. During thisperiod the EPA, through the Athens-ERL, sponsored anumber of model development and testing efforts(i.e., PTR, ARM, NPS, WEST) culminating in theHSPF model?Hydrologic Simulation Program-Fortran(Johanson et al, 1980). Barnwell and Johanson (1981)discuss the various model development and testingefforts leading to the initial release of HSPF in 1980;HSPF is currently in Release No. 9 (Johanson et al.,1984) as a result of numerous enhancements and codecorrections. The focus of the model development wasthe ability to represent contributions of sediment,pesticides, and nutrients from agricultural areas, andevaluate resulting water quality conditions at thewatershed scale considering both nonpointcontributions and instream water quality processes.Only the nonpoint capabilities of HSPF (i.e., PERLNDand IMPLND modules) are discussed in this report.
Coincident with the HSPF (and predecessor models)development, the U.S. Department of Agriculturethrough the Agricultural Research Service (ARS)assembled a group of ARS scientists to refine,improve, and integrate existing models into a packagefor representing runoff, sediment, nutrient, andpesticide runoff from agricultural fields. The effortwas initiated in 1977 and the resulting CREAMSmodel (Chemicals, Runoff, and Erosion fromAgricultural Management Systems) (Knisel, 1980) was
first published in 1980. Since its initial release,CREAMS has undergone testing and application anda companion version, called GLEAMS (Leonard et al.,1986) has been developed with special emphasis onvadose zone processes to represent movement ofchemicals to groundwater.
CREAMS and HSPF PERLND are the most detailed,operational models of agricultural runoff available atthe current time. In many ways, they are more alikethan they are different. Both models simulate runoffand erosion from field size areas, using differentmethods, and both simulate land surface and soilprofile chemical/biological processes (using similarmethods) that determine the fate and transport of pes-ticides and nutrients. Figure 1 shows the structure ofthe various subroutines that comprise the HSPFPERLND module; note that the AgrichemicalModules of PERLND perform the soilchemical/biological process simulation. Figure 2conceptually shows the structure and processessimulated for pesticides and nutrients in the ARMmodel which was the basis for the HSPF AgrichemicalModules. Figure 3 includes analogous diagrams forCREAMS, showing the structure of the model and theprocesses involved in estimating nutrient losses inrunoff and through leaching. Although the hydrologyand sediment algorithms are different for the twomodels, the soil processes that determine theavailability of chemicals for runoff and leaching arequite similar; both consider sorption/desorption,plant uptake, soil transformations (e.g.,mineralization, nitrification), attenuation/decay, etc.that control the fate and migration of chemicals in thesoil.
The two models differ primarily in their scope andlevel of detail, largely as a result of their historicalorigins. HSPF PERLND was derived from theStanford Watershed Model (SWM) which wassubsequently used as the basis for the HSP, ARM, andNPS models forming the predecessor components forHSPF. This model development effort originated inthe hydrologic research community with emphasis onnot only runoff modeling but also on watershed scalemodeling, including both runoff and hydraulicrouting needed for large watersheds and river basins.When EPA selected SWM as the basis for modelingnonpoint pollutant runoff, their ultimate goal was tobe able to evaluate the downstream water qualityimpacts of pesticide and nutrient runoff from agri-cultural lands. Consequently, HSPF considers allstreamflow components? surface runoff, interflow,
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baseflow?and their pollutant contributions (as shownin Figure 2), and then allows direct linkage of thesecontributions to an instream water quality model.Also, since HSP and NPS included algorithms forurban runoff loadings, and since most largewatersheds would include a variety of land use types,HSPF includes many of the simplified options (de-scribed above in Section 4.1) for modeling runoff fromany land category, including both pervious andimpervious urban categories.
On the other hand, CREAMS is a product of theagricultural research community with specificemphasis on representing soil profile and field-scaleprocesses at the level of detail appropriate for designof field-based agricultural management systems.Thus, CREAMS allows more detailed representationof field terraces, drainage systems, field topography,etc. and associated sediment erosion processes. Adetailed hydrology option is available, requiringbreakpoint rainfall (i.e., short time interval rainfall,hourly or less), or the popular SCS Curve Numberprocedure can be used with daily rainfall. Because ofits field-scale focus, CREAMS is limited torepresenting only surface runoff contributions;subsurface and leaching losses of chemicals aresimply removed from the system. The originalCREAMS documentation published in 1980 indicatedthat an effort to expand the model to a basin-scalewas underway. The SWAM model (DeCoursey, 1982;Alonso and DeCoursey, 1985) uses CREAMS as asource area component and adds the capabilities toconsider watershed and basin scale analyses;however, the model development effort is stillunderway at this time and SWAM is not currentlyconsidered `operational' in terms of documentationand use by non-developers (DeCoursey, personalcommunication, 1990). CREAMS has been used as thesource area model by a number of investigators forspecific studies (e.g. SWRRB (Williams et al., 1985),ADAPT (Ward et al., 1988), ARDBSN (Devaurs et al.,1988)) but no fully integrated, operational packagewith CREAMS for use at the basin/watershed scale isavailable comparable to HSPF.
4.2.2Other Non-Urban Nonpoint Models
Many other non-urban nonpoint models exist in themodeling community and have been used to varying CREAMS, but each has been used by the developers,a few, by outside users. A comprehensive review of allavailable and relevant nonpoint models was wellbeyond the scope of this review effort. Below wediscuss a few additional models, selected by theauthors, that have been applied more often than typi-cal `research' models, but they may not fully satisfythe definition of an `operational' model described inSection 2.2. The ANSWERS, AGNPS, PRZM,SWRRB/PRS, and UTM-TOX models are discussedbriefly below; a paragraph description is included inSection 5.2 and additional details are provided in theAppendix.
The ANSWERS model (Areal Nonpoint SourceWatershed Environment Response Simulation)developed by Beasley and Huggins (1981) at PurdueUniversity differs from most other nonpoint modelsin that it is an `event', distributed-parameter model,as opposed to a continuous, lumped parametermodeling approach. ANSWERS is designed primarilyto simulate single storm events, and requires that thewatershed be subdivided into grid elements withparameter information provided for each element;most continuous nonpoint models only requirespecification of average or mean parameter values fora watershed or subwatershed area. The ANSWERSapproach imposes greater computational burden andspatial data requirements, thus limiting most analysesto single `design' storms. However, the additionalspatial detail allows greater evaluation of source areaswithin a specific watershed area if required by theproblem assessment. ANSWERS is primarily a runoffand sediment model; the nutrient simulation is basedon simple correlations between concentration andsediment yield/runoff volume; soil nutrient processesare not simulated.
The AGNPS model (Agricultural Nonpoint SourcePollution Model) developed by the USDAAgricultural Research Service (Young et al., 1986) isone of the most recent nonpoint models and thus haslimited demonstrated experience. It is designed tosimulate runoff, sediment, and nutrients fromwatershed-scale areas for either single event orcontinuous periods. It uses a distributed approach,similar to ANSWERS, whereby the watershed area isdivided into cells, model computations are done atthe cell level, and runoff, sediment, and nutrients are
18
routed from cell to cell from the watershedboundaries to the outlet. AGNPS uses the SCS curvenumber approach combined with a unit hydrographrouting procedure, the Modified USLE, and simplecorrelations of extraction coefficients of nutrients inrunoff and sediment. AGNPS can accommodate pointsource inputs from feedlots, wastewater treatmentplants, and user-defined stream bank and gullyerosion. Because of its distributed approach, its spatialdata requirements are similar to ANSWERS.
The PRZM model (Pesticide Root Zone Model)(Carsel et al., 1984) was developed by the EPA Athenslaboratory for modeling the fate of pesticides withinthe crop root zone, and subsequent leaching togroundwater. However, it includes a runoff anderosion component based on the SCS curve numberand Modified USLE, respectively. PRZM representsdissolved, adsorbed, and vapor phase chemicalconcentrations in the soil by modeling the processesof surface runoff, erosion, evapotranspiration, plantuptake, soil temperature, pesticide decay,volatilization, foliar washoff, advection, dispersion,and decay. The most recent version of PRZM isincluded in an integrated root/vadose/groundwatermodel called RUSTIC recently released by the EPAAthens Laboratory (Dean et al., 1989). PRZM is cur-rently limited to simulation of organic chemicals likepesticides, but its runoff and erosion components aresimilar to many other nonpoint models.
The SWRRB model (Simulator for Water Resources inRural Basins) was developed by USDA (Williamset al., 1985; Arnold et al., 1989) for basin scale waterquality modeling. Its runoff (SCS curve number) anderosion (Modified USLE) components are similar tothe other nonpoint models, but SWRRB also includeschannel processes and subsurface flow components toallow representation of large basin areas. It performscalculations on a daily timestep, and simulateshydrology, crop growth, sediment erosion, sedimenttransport, and nitrogen/phosphorus/pesticidemovement in runoff. Its nutrient and pesticide
capabilities are derived from CREAMS; these are themost recent additions to the model and they are stillundergoing testing and validation by the developers.
The UTM-TOX model (Unified Transport Model forToxic Materials) (Patterson et al., 1983), developed bythe Oak Ridge National Laboratory for the U.S. EPAOffice of Pesticides and Toxic Substances, is amultimedia model that combines hydrologic,atmospheric, and sediment transport in one computercode. It is similar to HSPF in many ways, in terms ofits comprehensive scope; its representation of soil,land surface, and channel processes; and its use of theStanford Watershed Model as its hydrologic module.UTM-TOX provides a more detailed simulation ofsoil-plant processes, includes atmospheric transportand deposition, and is designed primarily for organicchemicals; no specific capabilities are included fornutrients or agricultural conditions. UTM-TOX, to ourknowledge, has had limited application, possiblybecause of its relatively complex nature and the lackof user support.
19Figure 1. Subroutine structure for HSPF PERLND.
20
21
Figure 2. ARM Model pesticide and nutrient and nutrient simulation included in HSPF PERLND.
22
23
Figure 3. Nutrient simulation in CREAMS.
24
25
Section 5.0
Nonpoint Source Runoff Quality Simulation Models and Methods
5.1 Urban Runoff Quality Models
5.1.1 Introduction
Four models (USGS, HSPF, STORM, SWMM) will bedescribed briefly at this point; extensive details aboutthe four models may be found in the Appendix. Thesefour models essentially make up the best choice offull-scale simulation models for urban areas. Othermodels have been adapted from SWMM (e.g., FHWA,RUNQUAL) and STORM (e.g., SEMSTORM) andgiven modified names, but the principles are fairlysimilar. Still other models, such as the Illinois StateWater Survey ILLUDAS model (Terstriep and Stall,1974) have sometimes been adapted for water qualitysimulation for a specific project (Noel and Terstriep,1982), but such modifications and quality proceduresremain undocumented, and the quality model can beconsidered not operational. At least two Europeanmodels are available that simulate water quality.These are described briefly at the end of this section.Finally, there are many models well known in thehydrologic literature, such as those developed by theHEC and SCS, that might be useful in the hydrologicaspect of water quality studies but that do notsimulate water quality directly. This review is limitedto models that directly simulate water quality. Ageneral comparison of model attributes is given inTable 4. This table includes the EPA Statistical Methodsince with the publication of the recent FHWA study,it can be considered a formalized procedure (Driscollet al., 1989). The constant concentration, spreadsheet,and regression approaches described earlier are moregeneric in nature and not included in Table 4, but theirattributes were provided in the earlier text.
5.1.2DR3M-QUAL
A version of the USGS Distributed Routing RainfallRunoff Model that includes quality simulation
(DR3M-QUAL) is available from that agency forgeneral use (Alley and Smith, 1982a, 1982b). Runoffgeneration and subsequent routing use the kinematicwave method, and parameter estimation assistance isincluded in the model. Quality is simulated usingbuildup and washoff functions, with settling of solidsin storage units dependent on a particle sizedistribution. The model has been used in some of theNURP studies that were conducted by the USGS (seethe Appendix and Alley, 1986). No microcomputerversion is available.
5.1.3HSPF
The Hydrological Simulation Program?Fortran(HSPF) is the culmination of hydrologic routines thatoriginated with the Stanford Watershed Model in 1966and eventually incorporated many nonpoint sourcemodeling efforts of the EPA Athens laboratory(Johanson et al., 1984). This model has been widelyused for non-urban nonpoint source modeling and isdescribed additionally in that section of this report aswell as in detail in the Appendix. The user's manualincludes information on all hydrologic and waterquality routines, including the IMPLND (imperviousland) segment for use in urban area. Additionalguidelines for application are provided by Donigian etal. (1984). The model has special provisions formanagement of time series that result fromcontinuous simulation. A microcomputer version isavailable.
5.1.4STORM
The first significant use of continuous simulation inurban hydrology came with the Storage, Treatment,Overflow, Runoff Model (STORM), developed by theCorps of Engineers Hydrologic Engineering Center(HEC, 1977; Roesner et al., 1974) for application to theSan Francisco master plan for CSO pollutionabatement. The HEC also provides application
26
guidelines (Abbott, 1977). The current versionincludes dry-weather flow input for combined sewersimulation. The support of the HEC led to the wideuse of STORM for planning purposes, especially forevaluation of the trade-off between treatment andstorage as control options for CSOs (e.g., Heaney etal., 1977). Statistics of long-term runoff and qualitytime series permit optimization of control measures.
STORM utilizes simple runoff coefficient, SCS andunit hydrograph methods for generation of hourlyrunoff depths from hourly rainfall inputs. No flowrouting is performed, but runoff may be routedthrough a constant-rate treatment device, with excessflow diverted to a storage device. Flows exceeding thetreatment rate cause CSOs when storage is filled. Thebuild-up and wash-off formulations are used forsimulation of six pre-specified pollutants. However,the model can be manipulated to provide loads forarbitrary conservative pollutants (e.g., Najarian et al.,1986). The model is hampered somewhat by lack of anoperational microcomputer version. However,various individual consultants have adapted thenonproprietary code to their own project needs.
5.1.5SWMM
The original version of the Storm Water ManagementModel (SWMM) was developed for EPA assingle-event model specifically for the analysis ofCSOs (Metcalf and Eddy et al., 1971), but its scope hasvastly broadened since the original release. Version 4(Huber and Dickinson, 1988; Roesner et al., 1988) ofthe model performs both continuous and single-eventsimulation throughout the whole model, can simulatebackwater, surcharging, pressure flow and loopedconnections (by solving the complete dynamic waveequations) in its Extran Block, and has a variety ofoptions for quality simulation, including traditionalbuild-up and wash-off formulations as well as ratingcurves and regression techniques. Subsurface flowrouting (constant quality) may be performed in theRunoff Block in addition to surface quantity andquality routing, and treatment devices may besimulated in the Storage/Treatment Block usingremoval functions and sedimentation theory. Ahydraulic design routine is included for sizing ofpipes, and a variety of regulator devices may besimulated, including orifices (fixed and variable),weirs, pumps, and storage. A bibliography of SWMMusage is available (Huber et al., 1986) that containsmany references to case studies.
SWMM is segmented into the Runoff, Transport,
Extran, Storage/Treatment and Statistics blocks forrainfall-runoff, routing, and statistical computations.Water quality may be simulated in all blocks exceptExtran, and metric units are optional. Since the modelis non-proprietary, portions have been adapted forvarious specific purposes and locales by individualconsultants and other federal agencies, e.g., FHWA. Amicrocomputer version is available.
5.1.6Two European Models
The four U.S. models discussed above do not takeadvantage of graphics and other ?user-friendly?capabilities of microcomputers. Two well-known andcommercially-available European models, MOUSEand Wallingford, are excellent examples of applicationof the full power of the microcomputer when used inconjunction with recent programming languages andgraphics hardware and software. Both models featuremenu-driven pre-processors for data input, graphicaldisplay and interactive editing of catchmentboundaries and sewer networks, and post-processingof predicted hydrographs and pollutographs,including graphical displays and statistical analysis.Although the quality algorithms are relatively simple,the hydrologic and hydraulic components of bothmodels are relatively sophisticated. The cost of eachmodel is approximately $15,000, including trainingand documentation but not the source code.
The Danish Hydraulic Institute, in cooperation withvarious other laboratories and private software firmshas produced the MOUSE (Modeling of UrbanSewers) model. Included in the package are modulesfor generation of runoff from rainfall, sewer routing(the S11S model, comparable to the SWMM ExtranBlock), and a simple quality routine that uses theconstant concentration approach (Jacobsen et al., 1984;Johansen et al., 1984). Further information on MOUSEis available from Danish Hydraulic Institute, AgernAlle 5, DK-2970 Hørsholm, Denmark.
The Wallingford model is maintained by HydraulicsResearch Ltd. in the United Kingdom. It also consistsof a cluster of modules, including runoff generationfrom rainfall (WASSP), simple and fully-dynamicsewer routing (WALLRUS and SPIDA, respectively),and a quality routine (MOSQITO)featuring processessimilar to those in SWMM (Henderson and Moys,1987). Further information on the group ofWallingford models is available from HydraulicsResearch Ltd., Wallingford, Oxfordshire OX10 8BA,United Kingdom.
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5.2 Non-urban Runoff Quality Models and
Methods
In this section we provide brief summaries of theprimary non-urban runoff quality models reviewed;as noted earlier additional details on each model areprovided in the Appendix. Below summaries arepresented for HSPF, CREAMS/GLEAMS, ANSWERS,AGNPS, PRZM, SWRRB, and UTM-TOX, and Table 5shows a comparison of selected model attributes andcapabilities.
5.2.1HSPF
The Hydrological Simulation Program?FORTRAN(HSPF) (Johanson et al., 1981; 1984) is acomprehensive package for simulation of watershedhydrology and water quality for both conventionaland toxic organic pollutants. HSPF incorporates thewatershed scale ARM and NPS models into a basin-scale analysis framework that includes fate andtransport in one-dimensional stream channels. It is theonly comprehensive model of watershed hydrologyand water quality that allows the integratedsimulation of land and soil contaminant runoffprocesses with instream hydraulic, watertemperature, sediment transport, nutrient, andsediment-chemical interactions. The runoff qualitycapabilities include both simple relationships (i.e.empirical buildup/washoff, constant concentrations)and detailed soil process options (i.e., leaching,sorption, soil attenuation and soil nutrienttransformations).
The result of this simulation is a time history of therunoff flow rate, sediment load, nutrient, pesticide,and/or user-specified pollutant concentrations, alongwith a time history of water quantity and quality atany point in a watershed. HSPF simulates threesediment types (sand, silt, and clay) in addition to asingle organic chemical and transformation productsof that chemical. The instream nutrient processesinclude DO, BOD, nitrogen and phosphorus reactions,pH, phytoplankton, zooplankton, and benthic algae.
The organic chemical transfer and reaction processesincluded are hydrolysis, oxidation, photolysis, biode-gradation, a volatilization, and sorption. Sorption ismodeled as a first-order kinetic process in which theuser must specify a desorption rate and anequilibrium partition coefficient for each of the three
solid types. Resuspension and settling of silts andclays (cohesive solids) are defined in terms of shearstress at the sediment-water interface. For sands, thecapacity of the system to transport sand at a par-ticular flow is calculated and resuspension or settlingis defined by the difference between the sand insuspension and the capacity. Calibration of the modelrequires data for each of the three solids types.Benthic exchange is modeled as sorption/desorptionand desorption/scour with surficial benthicsediments. Underlying sediment and pore water arenot modeled.
5.2.2CREAMS/GLEAMS
Chemicals, Runoff, and Erosion from AgriculturalManagement Systems (CREAMS) was developed bythe U.S. Department of Agriculture?AgriculturalResearch Service (Knisel, 1980; Leonard and Ferreira,1984) for the analysis of agricultural best managementpractices for pollution control. CREAMS is a fieldscale model that uses separate hydrology, erosion, andchemistry submodels connected together by pass files.
Runoff volume, peak flow, infiltration,evapotranspiration, soil water content, andpercolation are computed on a daily basis. If detailedprecipitation data are available then infiltration iscalculated at histogram breakpoints. Daily erosionand sediment yield, including particle size distri-bution, are estimated at the edge of the field. Plantnutrients and pesticides are simulated and storm loadand average concentrations of sediment-associatedand dissolved chemicals are determined in the runoff,sediment, and percolation through the root zone(Leonard and Knisel, 1984).
User defined management activities can be simulatedby CREAMS. These activities include aerial spraying(foliar or soil directed) or soil incorporation ofpesticides, animal waste management, andagricultural best management practices (minimumtillage, terracing, etc.).
Calibration is not specifically required for CREAMSsimulation, but is usually desirable. The modelprovides accurate representation of the various soilprocesses. Most of the CREAMS parameter values arephysically measurable. The model has the capabilityof simulating 20 pesticides at one time.
Groundwater Loading Effects of AgriculturalManagement Systems (GLEAMS) was developed bythe United States Department of
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Agriculture?Agriculture Research Service (Leonard etal., 1987) to utilize the management orientedphysically based CREAMS model (Knisel, 1980) andincorporate a component for vertical flux ofpesticides. GLEAMS is the vadose zone component ofthe CREAMS model.
GLEAMS consists of three major components namelyhydrology, erosion/sediment yield, and pesticides.Precipitation is partitioned between surface runoffand infiltration and water balance computations aredone on a daily basis. Surface runoff is estimatedusing the Soil Conservation Service Curve NumberMethod as modified by Williams and Nicks (1982).The soil is divided into various layers, with aminimum of 3 and a maximum of 12 layers of variablethickness are used for water and pesticide routing(Knisel et al., 1989)
5.2.3ANSWERS
Areal Nonpoint Source Watershed EnvironmentResponse Simulation (ANSWERS) was developed atthe Agricultural Engineering Department of PurdueUniversity (Beasley and Huggins, 1981). It is an eventbased, distributed parameter model capable ofpredicting the hydrologic and erosion response ofagricultural watersheds. Application of ANSWERSrequires that the watershed to be subdivided into agrid of square elements. Each element must be smallenough so that all important parameter values withinits boundaries are uniform. For a practical applicationelement sizes range from one to four hectares. Withineach element the model simulates the processes ofinterception, infiltration, surface storage, surface flow,subsurface drainage, and sediment drainage, andsediment detachment, transport, and deposition. Theoutput from one element then becomes a source ofinput to an adjacent element.
As the model is based on a modular programstructure it allows easier modification of existingprogram code and/or addition of user suppliedalgorithms. Model parameter values are allowed tovary between elements, thus, any degree of spatialvariability within the watershed is easily represented.
Nutrients (nitrogen and phosphorus) are simulatedusing correlation relationships between chemicalconcentrations, sediment yield and runoff volume. Aresearch version (Amin-Sichani, 1982) of the modeluses ? clay enrichment? information and a verydescriptive phosphorus fate model to predict total,particulate, and soluble phosphorus yields.
5.2.4AGNPS
Agricultural Nonpoint Source Pollution Model(AGNPS) was developed by the U.S. Department ofAgriculture?Agriculture Research Service (Young etal., 1986) to obtain uniform and accurate estimates ofrunoff quality with primary emphasis on nutrientsand sediments and to compare the effects of variouspollution control practices that could be incorporatedinto the management of watersheds.
The AGNPS model simulates sediments and nutrientsfrom agricultural watersheds for a single storm eventor for continuous simulation. Watersheds examinedby AGNPS must be divided into square workingareas called cells. Grouping of cells results in theformation of subwatersheds, which can beindividually examined. The output from the modelcan be used to compare the watershed examinedagainst other watersheds to point sources of waterquality problems, and to investigate possible solutionsto these problems.
AGNPS is also capable of handling point sourceinputs from feedlots, waste water treatment plantdischarges, and stream bank and gully erosion (userspecified). In the model, pollutants are routed fromthe top of the watershed to the outlet in a series ofsteps so that flow and water quality at any point inthe watershed may be examined. The ModifiedUniversal Soil Loss Equation is used for predictingsoil erosion, and a unit hydrograph approach used forthe flow in the watershed. Erosion is predicted in fivedifferent particle sizes namely sand, silt, clay, smallaggregates, and large aggregates.
The pollutant transport portion is subdivided into onepart handling soluble pollutants and another parthandling sediment attached pollutants. The methodsused to predict nitrogen and phosphorus yields fromthe watershed and individual cells were developed byFrere et al. (1980) and are also used in CREAMS(Knisel, 1980). The nitrogen and phosphorus calcula-tions are performed using relationships betweenchemical concentration, sediment yield and runoffvolume.
Data needed for the model can be classified into twocategories: watershed data and cell data. Watersheddata includes information applying to the entirewatershed which would include watershed size,number of cells in the watershed, and if running for asingle storm event then the storm intensity. The cell
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data includes information on the parameters based onthe land practices in the cell.
Additional model components that are underdevelopment are unsaturated/saturated zoneroutines, economic analysis, and linkage toGeographic Information System.
5.2.5PRZM
Pesticide Root Zone Model (PRZM) was developed atthe U.S. EPA Environmental Research Laboratory inAthens, Georgia by Carsel et al. (1984). It is a one-dimensional, dynamic, compartmental model that canbe used to simulate chemical movement inunsaturated zone within and immediately below theplant root zone. The model is divided into two majorcomponents namely, the hydrology (and hydraulics)and chemical transport. The hydrology componentwhich calculates runoff and erosion is based upon theSoil Conservation Service curve number procedureand the Universal Soil Loss Equation respectively.Evapotranspiration is estimated directly from panevaporation or by an empirical formula if panevaporation data is not available. Soil-water capacityterms including field capacity, wilting point, andsaturation water content are used for simulatingwater movement within the unsaturated zone.Irrigation application is also within modelcapabilities.
Pesticide application on soil or on the plant foliage areconsidered in the chemical transport simulation.Dissolved, adsorbed, and vapor-phase concentrationsin the soil are estimated by simultaneouslyconsidering the processes of pesticide uptake byplants, surface runoff, erosion, decay, volatilization,foliar washoff, advection, dispersion, and retardation.The user has two options to solve the transportequations using the original backward differenceimplicit scheme or the method of characteristics(Dean et al., 1989). As the model is dynamic it allowsconsiderations of pulse loads.
PRZM is an integral part of a unsaturated/saturatedzone model RUSTIC (Dean et al., 1989). RUSTIC (Riskof Unsaturated/Saturated Transport andTransformation of Chemical Concentrations) linksthree subordinate models in order to predict pesticidefate and transport through the crop root zone, andsaturated zone to drinking water wells throughPRZM, VADOFT, SAFTMOD.
VADOFT is a one-dimensional finite element model
which solves Richard's equation for water flow in theunsaturated zone. VADOFT can also simulate the fateand transport of two parent and two daughterproducts. SAFTMOD is a two-dimensional finiteelement model which simulates flow and transport inthe saturated zone in either an X-Y or X-Zconfiguration. The three codes PRZM, VADOFT, andSAFTMOD are linked together through an executionsupervisor which allows users to build models for sitespecific situation. In order to perform exposureassessments, the code is equipped with a Monte Carlopre and post processor (Dean et al., 1989).
5.2.6SWRRB
Simulator for Water Resources in Rural Basins(SWRRB) was developed by Williams et al. (1985),and Arnold et al. (1989) for evaluating basin scalewater quality. SWRRB operates on a daily time stepand simulates weather, hydrology, crop growth,sedimentation, and nitrogen, phosphorous, andpesticide movement. The model was developed bymodifying the CREAMS (Knisel, 1980) daily rainfallhydrology model for application to large, complex,rural basins.
Surface runoff is calculated using the SoilConservation Service Curve Number technique.Sediment yield is computed for each basin by usingthe Modified Universal Soil Loss Equation (Williamsand Berndt, 1977). The channel and floodplainsediment routing model is composed of twocomponents operating simultaneously (depositionand degradation). Degradation is based on Bagnold'sstream power concept and deposition is based on thefall velocity of the sediment particles (Arnold et al.,1989).
Return flow is calculated as a function of soil watercontent and return flow travel time. The percolationcomponent uses a storage routing model combinedwith a crack flow model to predict the flow throughthe root zone. The crop growth model (Arnold et al.,1989) computes total biomass each day during thegrowing season as a function of solar radiation andleaf area index.
The pollutant transport portion is subdivided into onepart handling soluble pollutants and another parthandling sediment attached pollutants. The methodsused to predict nitrogen and phosphorus yields fromthe rural basins are adopted from CREAMS (Knisel,1980). The nitrogen and phosphorus calculations areperformed using relationships between chemical
30
concentration, sediment yield and runoff volume. Thenutrient capabilities are still undergoing testing andvalidation at this time.
The pesticide component is directly taken from Holstand Kutney (1989) and is a modification of theCREAMS (Smith and Williams, 1980) pesticide model.The amount of pesticide reaching the ground or plantsis based on a pesticide application efficiency factor.Empirical equations are used for calculating pesticidewashoff which are based on threshold rainfallamount. Pesticide decay from the plants and the soilare predicted using exponential functions based onthe decay constant for pesticide in the soil, and halflife of pesticide on foliar residue.
The Pesticide Runoff Simulator (PRS) was developedfor the U.S. EPA Office of Pesticide and ToxicSubstances by Computer Sciences Corporation (1980)to simulate pesticide runoff and adsorption into thesoil on small agricultural watersheds. PRS is based onSWRRB. Thus, the PRS hydrology and sedimentsimulation is based on the USDA CREAMS model,and the SCS curve number technique is used topredict surface runoff. Sediment yield is simulatedusing a modified version of the Universal Soil LossEquation and a sediment routing model.
The pesticide component of PRS is a modified versionof the CREAMS pesticide model. Pesticide application(foliar and soil applied) can be removed byatmospheric loss, wash off by rainfall, and leachinginto the soil. Pesticide yield is divided into a solublefraction and an adsorbed phase based on anenrichment ratio.
The model includes a built in weather generator basedon temperature, solar radiation, and precipitationstatistics. Calibration is not specifically required, butis usually desirable.
5.2.7UTM-TOX
Unified Transport Model for Toxic Materials (UTM-TOX) was developed by Oak Ridge NationalLaboratory for the U.S. EPA Office of Pesticides andToxic Substances, Washington, D.C. (Patterson et al.,1983). UTM-TOX is a multimedia model thatcombines hydrologic, atmospheric, and sedimenttransport in one computer code. The model calculatesrates of flux of a chemical from release to theatmosphere, through deposition on a watershed,infiltration and runoff from the soil, to flow in astream channel and associated sediment transport.
From these calculations mass balances can beestablished, chemical budgets made, andconcentrations in the environment estimated. Theatmospheric transport model (ATM) portion of UTM-TOX is a Gaussian plume model that calculatesdispersion of pollutants emitted from point (stack),area, or line sources. ATM operates on a monthly timestep, which is longer than the hydrologic portion ofthe model and results in the use of an averagechemical deposition falling on the watershed.
The Terrestrial Ecology and Hydrology Model(TEHM) describes soil-plant water fluxes,interception, infiltration, and storm and groundwaterflow. The hydrologic portion of the model is from theWisconsin Hydrologic Transport Model (WHTM),which is a modified version of the StanfordWatershed Model (SWM). WHTM includes all of thehydrologic processes of the SWM and also simulatessoluble chemical movement, litter and vegetationinterception of the chemical, erosion of sorbedchemical, chemical degradation in soil and litter, andsorption in top layers of the soil. Stream transportincludes transfer between three sediment components(suspended, bed, and resident bed).
5.3 Discussion
The models discussed briefly here (and moreextensively in the Appendix) do not represent all ofthe modeling options available for runoff qualitysimulation, but they are certainly the most notable,widely used and most operational. Selection fromamong these models is often made on the basis ofpersonal preference and familiarity, in addition toneeded model capabilities. For example, for urbanmodeling various in-house versions of STORM arestill used by consultants even though the ?official?HEC version has not been updated since 1977,because these versions have been adapted to theneeds of the firm and because STORM has proven toprovide useful continuous simulation results.The USGS DR3M-QUAL model has perhaps beenused the least by persons outside that agency, but hasworked satisfactorily in several applicationsdocumented in the Appendix. Support for bothSTORM and DR3M-QUAL would be minimal.CREAMS has been used most extensively for field-scale agricultural runoff modeling because of itsagricultural origins and ties to the agriculturalresearch community.
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HSPF and SWMM are probably the most versatile andmost widely applicable of the models, with the nod toSWMM if the urban hydrology and hydraulics mustbe simulated in detail. On the other hand, the waterquality routines in HSPF for sediment erosion,pollutant interaction and groundwater quality aresuperior in HSPF, and the capability to efficientlyhandle all types of land uses and pollutant sources,(including urban and agriculture, point and non-point), is a definite advantage when needed for largecomplex basins. Both models appear somewhatoverwhelming in terms of size to the novice user, butonly the components of interest of either model needbe used in a given study, and the catchmentschematization can often be coarse for purposes ofsimulation of water quality at the outlet. Thus,although the installation of these models on amicrocomputer may occupy several megabytes of ahard disk, they may be applied in simple ways (i.e.,applied to a simplified schematization of thecatchment) with a significant reduction in datarequirements. Furthermore, the several quality model-ing options within SWMM permit simple conceptualwater quality simulation using constant concentrationand rating curves as well as the more formidablebuildup-washoff methods. Similarly for HSPF, theability to use the simple SWMM-type formulations forurban and non-agricultural areas, and detailedsoil/runoff process simulation for agricultural areasprovides the user with great flexibility in representingthe watershed system.
Continuing model development and testing withinthe agricultural research community will likely lead tofurther enhancements and development of many ofthe agricultural models, like CREAMS, SWRRB, andAGNPS. In fact, USDA has supported, and continuesto support, a wide range of model development workin individual research facilities, many of which are (orat least appear to be) very similar in terms of usingsimilar algorithms or model formulations (e.g. EPIC(Williams et al., 1984), Opus (V. Ferreira, 1989,personal communication), SWAM (DeCoursey, 1982).The SWRRB development effort appears to befocussing in on a middle ground (in terms ofcomplexity) between HSPF and the detailed field-scale models which are limited to small areas; its useof daily rainfall, as opposed to smaller time intervalmeasurements (usually hourly is needed for HSPF) isseen as a definite advantage by many users. However,most of
these efforts still focus primarily on agricultural areas,with limited abilities to be used in large, complexmulti-land use basins.
Regression, spreadsheet, statistical methods, andloading functions are most useful as screening tools.Indeed if the Statistical Method or EPA's ScreeningProcedures, indicate that there should be no waterquality problem (as defined by exceedance of aspecified concentration level with a specifiedfrequency), then more detailed water qualitysimulation may not be required at all. If sensitivityanalyses and `worst-case' evaluations further supportthe conclusions, detailed water quality modeling willprobably not be needed.
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Table 4. Comparison of urban model attributes
Model:Attribute DR3M-QUAL HSPF Statisticala STORM SWMM
Sponsoring agency USGS EPA EPA HEC EPA
Simulation typeb C,SE C,SE N/A C C,SE
No. pollutants 4 10 Any 6 10
Rainfall/runoff Y Y Nc Y Yanalysis
Sewer system flow Y Y N/A N Yrouting
Full, dynamic flow N N N/A N Yd
routing equations
Surcharge Ye N N/A N Yd
Regulators, overflow N N N/A Y Ystructures, e.g.,weirs, orificies, etc.
Special solids routines Y Y N N Y
Storage analysis Y Y Yf Y Y
Treatment analysis Y Y Yf Y Y
Suitable for screening S,D S,D S S S,D(S), design (D)
Available on micro- N Y Yg N Ycomputer
Data and personnel Medium High Medium Low Highrequirementsh
Overall model Medium High Medium Medium Highcomplexityi
aEPA procedure.bC = continuous simulation, SE = single event simulation.cRunoff coefficient used to obtain runoff volumes.dFull dynamic equations and surcharge calculations only in Extran Block of SWMM.eSurcharge simulated by storing excess inflow at upstream end of pipe. Pressure flow not simulated.fStorage and treatment analyzed analytically.gFHWA study, Driscoll et al. (1989)hGeneral requirements for model installation, familiarization, data require ments, etc. To be interpretted only very generally.iReflection of general size and overall model capabilities. Note that complex models may still be used to simulate very
simple systems with attendant minimal data requirements.
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Table 5. Comparison of non-urban model attributes
Attribute AGNPS ANSWERS CREAMS HSPF PRZM SWRRB UTM-TOX
Sponsoring Agency USDA Purdue USDA EPA EPA USDA ORNL &EPA
Simulation type C,SE SE C,SE C,SE C C C,SE
Rainfall/Runoff Y Y Y Y Y Y Yanalysis
Erosion Modeling Y Y Y Y Y Y Y
Pesticides Y N Y Y Y Y N
Nutrients Y Y Y Y N Y N
User-Defined N N N Y N N YConstituents
Soil ProcessesPesticides N N Y Y Y Y NNutrients N N Y Y N Y N
Multiple Land Type Y Y N Y N Y YCapability
Instream Water N N N Y N N YQuality Simulation
Available on Y Y Y Y Y Y NMicro-computer
Data and Personnel M M/H H H M M HRequirements
Overall Model M M H H M M/H HComplexity
Y = yes, N = no, M = Moderate, H = HighC = Continuous, SE = Storm Event
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Section 6.0
Brief Case Studies
How are quality processes being simulated in studiesof urban and rural runoff quality problems? Below,the authors draw upon their personal knowledge of afew ongoing and recently completed studies (listedalphabetically).
6.1 Urban Model Applications
6.1.1 Boston
CH2M-Hill (Gainesville, FL) used continuous SWMMmodeling for the development of TSS and BOD loadsfrom CSOs to Boston Harbor (Morrissey andHarleman, 1989). After first estimates from Sartor andBoyd (1972) and Pitt (1979), buildup and washofffunctions were calibrated to estimates of annual totalsbased on monitoring. A ? typical? five years of hourlyprecipitation data selected from 40 years of availablerecord were input to SWMM to develop CSO loads,and the effectiveness of street cleaning and catchbasincleaning BMPs was studied using the model (V.Adderly, CH2M-Hill, Inc., Gainesville, FL, PersonalCommunication, 1989).
6.1.2Delevan Lake, Wisconsin
A joint project of the USGS (Madison) and theUniversity of Wisconsin investigated suspendedsolids and phosphorus loads to 1800-ac Delevan Lakein southeastern Wisconsin (Walker et al., 1989). Aspreadsheet approach was implemented usingMultiplan, with unit load estimates for thesurrounding basin (agricultural, urban, industrial).The Universal Soil Loss Equation was used forsediment loads from agricultural areas. Somecalibration was possible using measurements on fourtributaries. The cost-effectiveness of agriculturalcontrol options was evaluated based on cost estimatesfor various agricultural BMPs.
6.1.3Hackensack River Basin
Pollution problems in the lower and estuarine portionof the Hackensack River in New Jersey are beingstudied by Najarian and Associates (Eatontown, NJ)using SWMM coupled with monitoring data fromfour CSO and five storm sewered areas (Huang andDiLorenzo, 1990; Najarian et al., 1990). The pollutantsof primary interest are BOD and ammonia for input toa dynamic receiving water quality model of the riverand estuary, with emphasis upon the relativecontributions of CSOs, separate storm sewered areasand point sources. Although rating curve results werevery good predictors for the monitored catchmentsfrom which they were derived, it was found that theycould not be extrapolated (transferred) to the ungagedcatchments. Hence, Michaelis-Menton buildup andexponential washoff parameters were calibrated forthe basins and transferable generalized coefficientsdeveloped as a function of land use. Intra-stormvariations were simulated in order to use SWMM todrive a short time increment dynamic model of theriver and estuary.
6.1.4Jacksonville
Camp, Dresser and McKee (Jacksonville) will useSWMM for quantity predictions and both aspreadsheet and SWMM or STORM with constantconcentrations for load estimates to the St. JohnsRiver (Camp, Dresser and McKee, Inc., 1989). Theconstant concentrations are based on NURP data andlimited Florida data. If SWMM or STORM is used todrive a receiving water quality model for the river,local data will be used for better calibration. At themoment, CDM feels that both quantity and qualitycontrol options can be compared on the basis ofpresent data, with a minimum of expensive localsampling.
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6.1.5Orlando
To help alleviate nonpoint source pollution to lakesdownstream from the Boggy Creek Watershed southof Orlando, Camp, Dresser and McKee (Orlando)developed a spreadsheet model to assess nutrientloadings resulting from existing and future land uses(Camp, Dresser and McKee, 1987). Runoff coefficientswere calibrated to match measured creek runoffvolumes, and EMCs as a function of land use wereestimated from sampling in Orlando and Tampa. Anoverall calibration factor was used to obtainagreement between the total estimated TN and TPloads produced by the product of flows and EMCs forthe various land uses and measured annual nutrientloads in Boggy Creek. Thus, relative contributionsfrom various land uses remained the same while theoverall loads were adjusted. BMP removal efficiencieswere applied in conjunction with changing land usesto obtain control strategies for future watersheddevelopment.
6.1.6Providence
SWMM is being used by Greeley and Hanson(Philadelphia) to simulate CSO loads from Providenceusing three monitored storms for calibration andverification (R. Janga, Greeley and Hansen, Inc.,Philadelphia, PA, Personal Communication, 1989).Quality is being simulated using constantconcentration in the Runoff Block and the qualityrouting routines in the Transport Block. SWMM maybe used to drive a receiving water model before theproject is completed. Extran is also being used tosimulate some of the overflow hydraulics.
6.1.7San Francisco Bay
Woodward-Clyde (Oakland) is using SWMM tosimulate loads from the Santa Clara Valley into SouthSan Francisco Bay (P. Mangarella, Woodward-ClydeConsultants, Oakland, CA, Personal Communication,1989). Measured runoff and flow data are being usedto calibrate the Runoff Block quantity routines, andconstant concentrations are being used (no buildup orwashoff) based on one year of monitoring of aselection of land use types. The model may not beused to drive a receiving water model but it will beused to compare alternatives to reduce loads of toxicsto the Bay.
6.1.8Tallahassee
The Northwest Florida Water Management District
(Havana, FL) is using SWMM to develop thestormwater master plan for Tallahassee and LeonCounty (R. Ortega, Northwest Florida WaterManagement District, Havana, FL, PersonalCommunication, 1989). Extensive use of the modelhas already been made for quantity predictions. Thepresent plan is to develop rating curve relationshipson the basis of considerable quality monitoring datagathered during the study for input into SWMM.BMPs will also be studied with the model, especiallystorage. Final control decisions will be made on thebasis of 28-year SWMM simulations using 15-minrainfall data.
6.2 Non-urban Model Applications
6.2.1 Chesapeake Bay Program
The EPA Chesapeake Bay Program has been using theHSPF model as the framework for modeling totalwatershed contributions of flow, sediment, andnutrients (and associated constituents such as watertemperature, DO, BOD, etc.) to the tidal region of theChesapeake Bay (Donigian et al., 1986; 1990). Thewatershed modeling represents pollutantcontributions from an area of more than 68,000 sq.mi., and provides input to drive a fully dynamicthree-dimensional, hydrodynamic/water qualitymodel of the Bay. The watershed drainage area isdivided into land segments and stream channelsegments; the land areas modeled include forest,agricultural cropland (conventional and conservationtillage systems), pasture, urban (pervious andimpervious areas), and uncontrolled animal wastecontributions. The stream channel simulation includesflow routing and oxygen and nutrient biochemicalmodeling (through phytoplankton) in order toaccount for instream processes affecting nutrientdelivery to the Bay.
Currently, buildup/washoff type algorithms are beingused for urban impervious areas, potency factors forall pervious areas, and constant (or seasonallyvariable) concentrations for all subsurfacecontributions and animal waste components.Enhancements are underway to utilize the detailedprocess (i.e. Agri-chemical modules) simulation forcropland areas to better represent the impacts ofagricultural BMPs. The watershed modeling is beingused to evaluate nutrient management alternatives forattaining a 40% reduction in nutrient loads deliveredto the Bay, as defined in a joint agreement among the
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governors of the member states.
6.2.2 Alachlor Special Review
The EPA Office of Pesticide Programs performed a`Special Review' of the herbicide alachlor, which iswidely used on corn and soybeans, to determineestimated concentrations in surface waters resultingfrom agricultural applications. HSPF was applied toselected watersheds in three separate agriculturalregions? Iowa River Basin, IA; Honey Creek, OH; andLittle River, GA?under different usage assumptions toevaluate a likely range of both mean annual andmaximum daily alachlor concentrations (Mulkey andDonigian, 1984). The modeling results provided inputto the human health risk assessment in which EPAdecided to allow continued use of alachlor in the U.S.
6.2.3CREAMS Application in Pennsylvania
The CREAMS model was applied by the University ofMaryland Department of Agriculture and Engineeringto selected subbasins of the Susquehanna andPotomac river basins in Pennsylvania to evaluate theeffects of agricultural BMPs on nutrient loadings tosurface water and to groundwater (Shirmohammadiand Shoemaker, 1988). The study was sponsored bythe Interstate Commission on the Potomac River toevaluate the relative nutrient loading impacts of awide range of potential BMPs, including no till,contouring, terracing, strip cropping, diversions,grass waterways, nutrient management, and variousjoint combination scenarios. Although the model wasapplied without calibration or observedrunoff/leaching data for comparison, the resultsshowed the relative effectiveness of the alternativesanalyzed. The study was performed in support ofefforts to evaluate alternative means of achieving the40% reduction goal of the Chesapeake BayAgreement.
6.2.4Use of SWRRB in NOAA's National CoastalPollutant Discharge Inventory
The National Oceanic and AtmosphericAdministration (NOAA) is using the SWRRB modelto evaluate pollutant loadings to coastal estuaries andembayments as part of its National Coastal PollutantDischarge Inventory (NOAA, 1987a; 1987b). SWRRBis being used for loadings from all non-urban areas,while a separate procedure is proposed for all urbanareas. SWRRB has been run for all major estuaries onthe East Coast, Gulf Coast, and West Coast for a widerange of pollutants.
6.2.5Use of AGNPS in Virginia
The Virginia Department of Soil and WaterConservation is applying the AGNPS model toevaluate sediment erosion and nutrient loadings fromland uses within the Owl Creek and Nomini Creekwatersheds. Both watersheds have been instrumentedto monitor runoff quality from forest, agriculturalcropland, and animal feedlot areas. AGNPS is beingapplied to the watersheds to analyze potential re-ductions in nutrient loadings for alternativemanagement scenarios (M. Flagg, Virginia Dept. ofSoil and Water Conservation, Richmond, VA, PersonalCommunication, 1990). The results of theseapplications, and planned use of the calibrated HSPFmodel resulting from the Chesapeake Bay Programapplication noted above, will be used to evaluate themeans of achieving Virginia's 40% nutrient reductionsrequired by the Chesapeake Bay Agreement.
6.2.6Use of HSPF in Metropolitan Washington
The Metropolitan Washington Council ofGovernments has used HSPF in a number ofmodeling studies to evaluate water quality impacts ofnonpoint sources, potential changes resulting fromproposed urban stormwater management practices,and water quality changes resulting from alternativewastewater treatment levels (Sullivan and Schueler,1982; Schueler, 1983; Metropolitan WashingtonCouncil of Governments, 1985). Studies onPiscataway and Seneca Creeks demonstratedreasonable agreement with observed instream waterquality variables, and then the calibrated model wasused to analyze water quality impacts of alternativescenarios including increased street sweeping,stormwater detention, and stormwater treatment.Since the watershed areas are primarily urban, thebuildup/washoff algorithms were used to calculateloadings from all land areas. In a separate study, theHSPF instream module was used with pre-definednonpoint and point source loadings to evaluate theimpacts of proposed alternative wastewater treatmentlevels on the segment of the Potomac River nearWashington, D.C.
6.2.7Patuxent River Nonpoint SourceManagement Study
The Maryland Department of the Environment isconducting a study of the Patuxent River to quantifynonpoint source contributions and evaluatealternative means of improving downstream water
37
quality in the Patuxent River Estuary (Summers,1986). The study includes a 7-year monitoringprogram that involves observations of runoff quantityand quality at both field size (single land use)locations and instream, multi-land use sites. HSPF isbeing applied to calculate nonpoint loadings from theforest, agricultural, and urban land areas of thewatershed and the instream water quality throughoutthe river system. The Patuxent is a microcosm of thelarger Chesapeake Bay, a complex watershed withmultiple land uses, point and nonpoint sources, andreservoirs draining to a tidal estuary. Like the largerChesapeake Bay study, both simple and complexnonpoint runoff algorithms will be used to representall land uses and effects of potential managementpractices.
6.2.8 European Case Studies in Applicationof the CREAMS Model
Svetlosanov and Knisel (1982) provide a compendiumof case studies describing applications of CREAMS inEurope. The work was sponsored by the InternationalInstitute of Applied Systems Analysis in Laxenburg,Austria, with the dual objectives of demonstrating theuse of CREAMS for quantitative evaluation of theimpacts of agricultural management in differentcountries, and performing model testing andvalidation studies. The report describes applicationsin Finland, West Germany, Poland, Sweden, theUnited Kingdom, and the Soviet Union; comparisonsof model results with observations were made in afew of the studies, while in others CREAMS was usedwithout comparison to observed data to investigatealternative practices. Analysis of the case studiesidentified some of the potential benefits from themodel applications, and elucidated some of thegeneric model weaknesses (e.g., smowmelt) and spe-cific refinements needed for European conditions.
38
39
Section 7.0
Summary and Recommendations for Nonpoint Source Runoff Quality Modeling
Simulation of runoff quality will increase inimportance as regulation and control of nonpointsources increases in the next several years. Theimplementation of Section 405 of the Clean Water Actis especially important if stormwater outfalls will berequired to have NPDES permits. The EPA iscurrently establishing guidelines for data collection,quality monitoring and forms of analysis such thaturban areas can meet their obligations under theseregulations. Waste load allocations and appropriatecontrol strategies required under Section 303 (d) and319 will demand more detailed analyses of nonpointcontributions for comprehensive water qualitymanagement.
Some form of modeling will almost assuredly becomepart of routine analyses performed at some portion ofthe thousands upon thousands of CSO andstormwater discharge locations around the country.Several modeling options exist, but none of them aretruly ?deterministic? in the sense of fullycharacterizing the physical, chemical and biologicalmechanisms that underlie conceptual buildup,erosion, transport and degradation processes thatoccur in an urban drainage system. Even if fullydeterministic models were available, it is doubtfulthat they could be routinely applied withoutcalibration data. But this is essentially true of almostall methods. Because a method is simple, e.g.,constant concentration, does not make it more correct.Rather, the assumption is made that there will besome error in prediction regardless of the method,and there may be no point in compiling manyhypothetical input parameters for a more complexmodel lacking a guarantee of a better prediction. Forexample, a study in Denver showed that regressionequations could predict about as well asDR3M-QUAL given the available quality information(Ellis and Lindner-Lunsford, 1986). Butphysically-based (conceptual) models do have certainadvantages, discussed later.
Physically-based urban models depend uponconceptual buildup and washoff processesincorporated into the quality algorithms. Such modelshave withstood the test of time and have been appliedin major urban runoff quality studies. However, therelative lack of fundamental data on buildup andwashoff parameters has lead to simpler methods moreoften being applied, starting with the assumption of aconstant concentration and becoming more complex.For example, the derived distribution approach of theEPA Statistical Method provides very useful screeninginformation with minimal data?but more than arerequired by just assuming a constant concentration.With the mass of NURP and other data, regressionapproaches are now more viable but still subject to theusual restrictions of regression analysis. Spreadsheetsare ubiquitous on microcomputers and serve as aconvenient mechanism to implement several of thesimple approaches, especially those that rely uponsets of coefficients and EMCs as a function of land useor other demographic information. For example, theEPA Screening Procedures could easily beimplemented in a spreadsheet format, and would bean appropriate tool for nonpoint source wasteloadallocation assessments, at least for screeningpurposes.
Minimal data requirements and ease of applicationare the principal advantages of simpler simulationmethods (constant concentration, statistical,regression, loading functions). However, in spite oftheir more complex data requirements, conceptualmodels (DR3M-QUAL, HSPF, STORM, SWMM,CREAMS, SWRRB) have advantages in terms of simu-lation of routing effects and control options as well assuperior statistical properties of continuous timeseries. For example, the EPA Statistical Methodassumes that stream flow is not correlated with theurban runoff flow. This may or may not be true in agiven situation, but it is not necessary to require suchan assumption when running a model such as HSPFor SWMM. The urban and non-urban conceptual
40
models discussed in detail all have a means ofsimulating storage and treatment effects, and/orimpacts of a significant number of managementoptions. Other than a constant removal, this is dif-ficult to do with the simpler methods. The conceptualmodels generally have very much superior hydrologicand hydraulic simulation capabilities (not true forSTORM except that it can also use real rainfallhyetographs as input). This alone usually leads tobetter prediction of loads (product of flow timesconcentration). It should also be borne in mind thateven complex models such as SWMM can be run withminimal quality (and quantity) data requirements,such as using only a constant concentration. Finally,some of the case studies imply that transferability ofcoefficients and parameters is easier with buildup andwashoff than with rating curve and constantconcentration methods.
If a more complex conceptual model is to be applied,which one should it be from among the onesdescribed herein? SWMM is certainly the most widelyused and probably the most versatile for urban areas,but all have their advocates. HSPF may be moreappropriate in large multiple land use watersheds, inareas with more open space where groundwatercontributions increase in importance, where rainfall-induced erosion occurs, or where quality interactionsare important along the runoff pathway. Thesimplicity of STORM remains attractive, and variousconsultants have utilized their own version as aplanning tool. The USGS DR3M-QUAL model hasbeen successfully applied in several USGS studies buthas not seen much use outside the agency. It containsuseful techniques for quality calibration.
SWMM and HSPF retain limited support from theEPA Center for Exposure Assessment Modeling(CEAM) at Athens, Georgia, and a similar level ofsupport is available for CREAMS from the USDASoutheast Watershed Research Laboratory in Tifton,Georgia. Unfortunately, this support is limited mainlyto distribution and implementation on a computersystem. STORM and DR3M-QUAL will remain useful,but it is unlikely that either of these two models willenjoy enhancements or support from their sponsoringagencies in the near future. Extramural support for allmajor operational models is highly desirable for main-tenance and improvements, especially in light of thegeneral models in nonpoint source studies in the U.S.No model can exist for long without continuingsustenance in the form of user support,maintenance, and refinements in response tochanging technology. All agencies who have spon-
sored, or are currently sponsoring, modeldevelopment efforts need to recognize the criticalimportance of these activities if their efforts are toproduce `operational' models with associated wide-spread usage.
Agricultural model development will continue largelyunder the continuing sponsorship of the U.S.D.A.Currently, CREAMS is the most used model forstrictly agricultural land, but a number of modeldevelopment efforts are ongoing at variousagricultural research stations across the country. Thecontinuing development and testing of the SWRRBmodel will likely lead to its increased use in a numberof non-urban studies; its use of a daily time step isattractive to users because of the less intensive dataneeds than for HSPF. However, for large complexwatersheds, involving both urban and non-urbanareas, HSPF will remain the model of choice for manyusers. Ongoing agricultural research will likely lead toimproved understanding of processes, with improvedalgorithms that should be incorporated into currentmodels. For example, the U.S.D.A. effort to develop amore process-oriented replacement for the USLE (i.e.the WEPP?Watershed Erosion Prediction Project) willlikely lead to improved soil erosion algorithms thatmay be appropriate for incorporation into currentmodels.
What is a reasonable approach to simulation of runoffquality? The main idea, for both urban and non-urbanareas, is to use the simplest approach that will addressthe project objectives at the time. This usually meansto start simple with a screening tool such as constantconcentration (usually implemented in a spreadsheet),regression, statistical, or loading function approach. Ifthese methods indicate that more detailed study isnecessary or if they are unable to address all theaspects of the problem, e.g., the effectiveness ofcontrol options or management alternatives, then oneof the more complex models must be run. No methodcurrently available (or likely to be available) canpredict absolute (accurate) values of concentrationsand loads without local calibration data, includingcomplex buildup and washoff models for urban areas,and soil process models for agricultural croplands.Thus, if a study objective is to provide input loads toa receiving water quality model, local site-specificdata will probably be required. On the other hand,several methods and models might be able tocompare the relative contributions from differentsource areas, or to determine the relative effectivenessof control and/or management options (if the controlscan be characterized by simple removal fractions).
41
When used for purposes such as these, the methods,including buildup and washoff models, can usually beapplied on the basis of NURP data (for urban models)and/or the best currently available source of qualitydata, such as data from agricultural research stationsfor the non-urban models.
When properly applied and their assumptionsrespected, models can be tremendously useful tools inanalysis of urban and non-urban runoff qualityproblems. Methods and models are evolving thatutilize the large and currently expanding data base ofquality information. As increasing attention is paid torunoff problems in the future, the methods andmodels can only be expected to improve.
Section 8.0
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Cincinnati, OH.
Heaney, J.P., W.C. Huber, H. Sheikh, M.A. Medina,J.R. Doyle, W.A. Peltz and J.E. Darling. 1975. UrbanStormwater Management Modeling and Decision-Making. EPA-670/2-75-022 (NTIS PB-242290), U.S.Environmental Protection Agency, Cincinnati, OH.
Henderson, R.J. and G.D. Moys. 1987. ?Developmentof a Sewer Flow Quality Model for the UnitedKingdom,? Topics in Urban Storm Water Quality,Planning and Management, Proc. Fourth Int.Conference on Urban Storm Drainage, W. Gujerand V. Krejci, eds., Ecole Polytechnique Federale,Lausanne, Switzerland, pp. 201-207.
Howard, C.D.D. 1976. Theory of Storage andTreatment Plant Overflows. J. EnvironmentalEngineering Division, Proc. ASCE,102(EE4):709-722.
Huang, P. and J.L. DiLorenzo. 1990. ?LowerHackensack River Watershed Planning UsingSWMM-4,? Proc. Stormwater and Water QualityModeler Users Group Meeting, Eatontown, NJ,EPA Report in press, Environmental ProtectionAgency, Athens, GA.
Huber, W.C. 1980. Urban Wasteload Generation byMultiple Regression Analysis of Nationwide UrbanRunoff Data. Proc. Workshop on Verification ofWater Quality Models, R.V. Thomann and T.O.Barnwell, eds., EPA-600/9-80-016, pp. 167-175(NTIS PB80-186539), U.S. Environmental ProtectionAgency, Athens, GA.
Huber, W.C. and J.P. Heaney. 1982. AnalyzingResiduals Generation and Dis charge from Urbanand Non-urban Land Surfaces. In: AnalyzingNatural Systems, Analysis for RegionalResiduals?Environmental Quality Management, D.J.Basta and B.T. Bower, eds., Resources for theFuture, Johns Hopkins University Press, Baltimore,MD (also available from NTIS as PB83-223321),Chapter 3, pp. 121-243.
Huber, W.C., J.P. Heaney, D.A. Aggidis, R.E.Dickinson, K.J. Smolenyak and R.W. Wallace. 1982.Urban Rainfall-Runoff-Quality Data Base,EPA-600/2-81-238 (NTIS PB82-221094), U.S.Environmental Protection Agency, Cincinnati, OH.
Huber, W.C. 1985. Deterministic Modeling of UrbanRunoff Quality. In: Urban Runoff Pollution, H.C.
Torno, J. Marsalek and M. Desbordes, eds., NATOASI Series, Series G: Ecological Sciences, 10:167-242, Springer-Verlag, New York, NY.
Huber, W.C. 1986. Modeling Urban Runoff Quality:State of the Art. Proceedings of Conference onUrban Runoff Quality, Impact and QualityEnhancement Technology, B. Urbonas and L.A.Roesner, eds., Engineering Foundation, ASCE, pp.34-48, New York, NY.
Huber, W.C., J.P. Heaney and B.A. Cunningham. 1986.Storm Water Management Model (SWMM)Bibliography. EPA/600/3-85/077 (NTISPB86-136041/AS), U.S. Environmental ProtectionAgency, Athens, GA.
Huber, W.C. and R.E. Dickinson. 1988. Storm WaterManagement Model User's Manual, Version 4.EPA/600/3-88/001a (NTIS PB88-236641/AS), U.S.Environmental Protection Agency, Athens, GA.
Hydrologic Engineering Center. 1977. Storage,Treatment, Overflow, Runoff Model, STORM,User's Manual. Generalized Computer Program723-S8-L7520, Corps of Engineers, Davis, CA.
Hydroscience, Inc. 1979. A Statistical Method forAssessment of Urban Storm WaterLoads? Impacts?Controls. EPA-440/3-79-023 (NTISPB-299185/9), U.S. Environmental ProtectionAgency, Washington, DC.
Jacobsen, P., P. Harremoes and C. Jakobsen. 1984.?The Danish Stormwater Modelling Package: TheSVK-System,? Proc. Third Int. Conference onUrban Storm Drainage, P. Balmer, P-A. Malmqvistand A. Sjoberg, eds., Chalmers University,Goteborg, Sweden, 2:453-562.
James, L.D. and S.J. Burges. 1982. Selection,Calibration, and Testing of Hydrologic Models. In:Hydrologic Modeling of Small Watersheds, C.T. Haan,H.P. Johnson and D.L Brakensiek, eds., MonographNo. 5, Chapter 11, pp. 435-472, American Society ofAgricultural Engineers, St. Joseph, MI.
Johansen, N.B., J.J. Linde-Jensen and P. Harremoes.1984. ?Computing Combined System OverflowBased on Historical Rain Series,? Proc. Third Int.Conference on Urban Storm Drainage, P. Balmer, P-A. Malmqvist and A. Sjoberg, eds., ChalmersUniversity, Goteborg, Sweden, 3:909-918.
45
Johanson, R.C., J.C. Imhoff, J.L. Kittle and A.S.Donigian. 1984. Hydrological SimulationProgram?Fortran (HSPF): User's Manual forRelease 8. EPA-600/3-84-066. U.S. EnvironmentalProtection Agency, Athens, GA.
Kibler, D.F., ed. 1982. Urban Stormwater Hydrology.American Geophysical Union, Water ResourcesMonograph 7, Washington, DC.
Knisel, W. (ed). 1980. CREAMS: A Field-Scale Modelfor Chemicals, Runoff, and Erosion fromAgricultural Management Systems. U.S.Department of Agriculture. Conservation ResearchReport No. 26, 640 pp.
Lager, J.A., W.G. Smith, W.G. Lynard, R.M. Finn andE.J. Finnemore. 1977. Urban StormwaterManagement and Technology: Update and Users'Guide. EPA-600/8-77-014 (NTIS PB-275654), U.S.Environmental Protection Agency, Cincinnati, OH.
Li, W., D.E. Merrill and D.A. Haith. 1989. LoadingFunctions for Pesticide Runoff. J. WPCF 62(1):16-26.
Loganathan, V.G. and J.W. Delleur. 1984. Effects ofUrbanization on Frequencies of Overflows andPollutant Loadings from Storm Sewer Overflows:A Derived Distribution Approach. Water ResourcesResearch, 20(7):857-865.
Lorber, M.N. and L.A. Mulkey. 1982. An Evaluation ofThree Pesticide Runoff Loading Models. J. Environ.Qual. 11(3):519-529.
Manning, M.J., R.H. Sullivan and T.M. Kipp. 1977.Nationwide Evaluation of Combined SewerOverflows and Urban Stormwater Discharges?Vol.III: Characteristics of Discharges.EPA-600/2-77-064c (NTIS PB-272107), U.S. En-vironmental Protection Agency, Cincinnati, OH.
Metropolitan Washington Council of Governments.1985. Documentation of the Calibration andVerification of the Upper Potomac River Model.Prepared for Maryland Office of EnvironmentalPrograms, Baltimore, MD.
McElroy, A.D., S.Y. Chiu, J.W. Nebgen, A. Aleti andR.W. Bennett. 1976. Loading Functions forAssessment of Water Pollution from Non-PointSources. EPA-600/2-76-151 (NTIS PB-253325), U.S.Environmental Protection Agency, Washington,DC.
Metcalf and Eddy, Inc. 1971. University of Florida,and Water Resources Engineers, Inc., Storm WaterManagement Model, Volume I?Final Report. EPAReport 11024DOC07/71 (NTIS PB-203289), U.S.Environmental Protection Agency, Washington,DC.
Miller, R.A., H.C. Mattraw, Jr. and M.E. Jennings.1978. Statistical Modeling of Urban Storm WaterProcesses, Broward County, Florida. Proc.International Symposium on Urban Storm WaterManagement, University of Kentucky, Lexington,KY. pp. 269-273.
Mills, W.B., V.H. Colber and J.D. Dean. 1979. Hand-Held Calculator Programs for Analysis of RiverWater Quality Interactions. Tetra Tech, Inc.,Lafayette, CA.
Mills, W.B., J.D. Dean, D.B. Porcella, S.A. Gherini,R.J.M. Hudson, W.E. Frick, G.L. Rupp and G.L.Bowie. 1982. Water Quality Assessment: AScreening Procedure for Toxic and ConventionalPollutants. EPA-600/6-82-004a and b. Volumes Iand II. U.S. Environmental Protection Agency.
Mockus, J. 1972. Estimation of Direct Runoff fromStorm Rainfall. In: National EngineeringHandbook, Sec. 4, Hydrology. U.S. SoilConservation Service, Washington, DC.
Morrissey, S.P. and D.R.F. Harleman. 1989.?Technology and Policy Issues Involved in theBoston Harbor Cleanup,? Parsons LaboratoryReport No. 327, M.I.T., Cambridge, MA.
Mulkey, L.A. and A.S. Donigian, Jr. 1984. ModelingAlachlor Behavior in Three Agricultural RiverBasins. U.S. Environmental Protection Agency,Environmental Research Laboratory, Athens, GA.
Mulkey, L.A., R.F. Carsel and C.N. Smith. 1986. Devel-opment, Testing, and Applications of NonpointSource Models for Evaluation of Pesticides Risk tothe Environment. In: Agricultural Nonpoint SourcePollution: Model Selection and Application. Giorgini,A. and F. Zingales (eds). Developments in Environ-mental Modeling Series (No. 10). Elsevier SciencePublishers, Amsterdam, The Netherlands.
Najarian, T.O., T.T. Griffin and V.K. Gunawardana.1986. Development Impacts on Water Quality: ACase Study. J. Water Resources Planning and
46
Management, ASCE, 112(1):20-35.
Najarian, T.O., V.K. Gunawardana, R.V. Ram and D.Lai. 1990. ?Modeling the Impact of Point/NonpointDischarges on Lower Hackensack River WaterQuality,? Proc. Stormwater and Water QualityModeler Users Group Meeting, Eatontown, NJ,EPA Report in press, Environmental ProtectionAgency, Athens, GA.
National Oceanic and Atmospheric Administration.1987a. The National Coastal Pollutant DischargeInventory: Urban Runoff Methods Document.Office of Oceanography and Marine Assessment,NOAA, Rockville, MD.
National Oceanic and Atmospheric Administration.1987b. The National Coastal Pollutant DischargeInventory: Nonurban Runoff Methods Document.Office of Oceanography and Marine Assessment,NOAA, Rockville, MD.
Noel, D.D. and M.L. Terstriep. 1982. Q-ILLUDAS?AContinuous Urban Runoff/Washoff Model. Proc.Int. Symp. on Urban Hydrology, Hydraulics, andSediment Control, UKY BU128, University ofKentucky, Lexington, KY. pp. 1-10.
Noel, D.D., M.L. Terstriep and C.A. Chenoweth. 1987.Nationwide Urban Runoff Program Data Reports.Illinois State Water Survey, Dept. of Energy andNatural Resources, Champaign, IL.
Novotny, V. and G. Chesters. 1981. Handbook ofNonpoint Pollution: Sources and Management. VanNostrand Reinhold Company, New York, NY. 555pp.
Novotny, V. 1985. Discussion of ?Probability Model ofStream Quality Due to Runoff,? by D.M. Di Toro.Journal of Environmental Engineering, ASCE,111(5):736-737.
Pisano, W.C. and C.S. Queiroz. 1977. Procedures forEstimating Dry Weather Pollutant Deposition inSewerage Systems. EPA-600/2-77-120 (NTISPB-270695), U.S. Environmental Protection Agency,Cincinnati, OH.
Pitt, R. 1979. Demonstration of Non-Point PollutionAbatement Through Improved Street CleaningPractices. EPA-600/2-79-161 (NTIS PB80-108988),U.S. Environmental Protection Agency, Cincinnati,OH.
Roesner, L.A., J.A. Aldrich and R.E. Dickinson. 1988.Storm Water Management Model User's ManualVersion 4: Addendum I, EXTRAN.EPA/600/3-88/001b (NTIS PB88-236658/AS), U.S.Environmental Protection Agency, Athens, GA.
Roesner, L.A. and S.A. Dendrou. 1985. Discussion of?Probability Model of Stream Quality Due toRunoff,? by D.M. Di Toro. Journal of Environ-mental Engineering, ASCE, 111(5):738-740.
Roesner, L.A., H.M. Nichandros, R.P. Shubinski, A.D.Feldman, J.W. Abbott and A.O Friedland. 1974. AModel for Evaluating Runoff-Quality inMetropolitan Master Planning. ASCE Urban WaterResources Research Program, Technical Memo. No.23 (NTIS PB-234312), ASCE, New York, NY.
Sartor, J.D. and G.B. Boyd. 1982. Water PollutionAspects of Street Surface Contaminants.EPA-R2-72-081 (NTIS PB-214408), U.S. Environ-mental Protection Agency, Washington, DC.
Schueler, T.R. 1983. Seneca Creek WatershedManagement Study, Final Report, Volumes I and II.Metropolitan Washington Council of Governments,Washington, DC.
Schueler, T.R. 1987. Controlling Urban Runoff: APractical Manual for Planning and DesigningUrban BMPs. Metropolitan Information Center,Metropolitan Washington, Council ofGovernments, Washington, DC.
Shirmohammadi, A. and L.L. Shoemaker. 1988.Impact of Best Management Practices on WaterQuality in Pennsylvania. ICPRB Report No. 88-7.Prepared for Interstate Commission on thePotomac River Basin, Rockville, MD.
Small, M.J. and D.M. Di Toro. 1979. Stormwater Treat -ment Systems. Journal of the EnvironmentalEngineering Division. Proc. ASCE,105(EE3):557-569.
Sullivan, M.P. and T.R. Schueler. 1982. ?ThePiscataway Creek Watershed Model: A Stormwaterand Nonpoint Source Management Tool.? In:Proceedings Stormwater and Water QualityManagement Modeling and SWMM Users GroupMeeting. October 18-19, 1982. Paul E. Wisner ed.,Univ. of Ottawa, Dept. Civil Engr., Ottawa, Ont.,Canada.
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Svetlosanov, W. and W.G. Knisel (eds.). 1982.European and United States Case Studies inApplication of the CREAMS Model. CP-82-S11.International Institute of Applied SystemsAnalysis, Laxenburg, Austria. pp. 147.
Summers, R.M. 1986. HSPF Modeling of the PatuxentRiver Basin. Maryland Office of EnvironmentalPrograms, Baltimore, MD. Presented at ChesapeakeBay Research Conference, March 1986.
Tasker, G.D. and N.E. Driver. 1988. NationwideRegression Models for Predicting Urban RunoffWater Quality at Unmonitored Sites. WaterResources Bulletin, 24(5):1091-1101.
Terstriep, M.L. and J.B. Stall. 1974. The Illinois UrbanDrainage Area Simulator, ILLUDAS. Bulletin 58,Illinois State Water Survey, Urbana, IL.
U.S. Forest Service. 1980. An Approach to Water Re-sources Evaluation of Non-Point SilviculturalSources (A Procedural Handbook). EPA-600/8-80-012. U.S. Environmental Protection Agency,Athens, GA. 861 pp.
Viessman, W., Jr., G.L. Lewis and J.W. Knapp. 1989.Introduction to Hydrology. Third Edition, Harperand Row, New York, NY.
Walker, J.F., S.A. Pickard and W.C. Sonzogni. 1989.Spreadsheet Watershed Modeling for Nonpoint-Source Pollution Management in a Wisconsin Area.Water Resources Bulletin, 25(1):139-147.
Ward, A.D., C.A. Alexander, N.R. Fausey and J.D.Dorsey. 1988. In: Modeling Agricultural, Forest, andRangeland Hydrology. Proceedings of the 1988 Inter-national Symposium. American Society ofAgricultural Engineers. ASAE Publication No. 07-88, pp. 129-141, St. Joseph, MI.
Water Pollution Control Federation. 1989. Manual ofPractice on CSO Pollution Abatement, No. FD-17,WPCF, Alexandria, VA.
Whipple, D.J., N.S. Grigg, T. Grizzard, C.W. Randall,R.P. Shubinski and L.S. Tucker. 1983. StormwaterManagement in Urbanizing Areas. Prentice-Hall,Englewood Cliffs, NJ.
Williams, J.R. 1975. Sediment-Yield Prediction withUniversal Soil Loss Equation Using Runoff Energy
Factor. In: Present and Prospective Technology forPredicting Sediment Yields and Sources. U.S. Dept.of Agricultures. ARS-S-40.
Williams, J.R., C.A. Jones and P.T. Dyke. 1984. AModeling Approach to Determining theRelationship Between Erosion and SoilProductivity. Trans. ASAE 27(1):129-144.
Williams, J.R., A.D. Nicks and J.G. Arnold. 1985.Simulator for Water Resources in Rural Basins.ASCE J. Hydraulic Engineering. 111(6):970-986.
Wischmeier, W.H. and D.D. Smith. 1978. PredictingRainfall-Erosion Losses: A Guide to ConservationPlanning. Agricultural Handbook No. 537. U.S.Dept. of Agriculture, Agricultural Research Service.
Woodward-Clyde Consultants. 1989. SynopticAnalysis of Selected Rainfall Gages Throughout theUnited States. Report to EPA, Woodward-ClydeConsultants, Oakland, CA.
Zison, S.W. 1980. Sediment-Pollutant Relationships inRunoff from Selected Agricultural, Suburban andUrban Watersheds. EPA-600/3-80-022, U.S.Environmental Protection Agency, Athens, GA.
Zison, S.W., K. Haven and W.B. Mills. 1977. WaterQuality Assessment: A Screening Methodology forNondesignated 208 Areas. EPA-600/6-77-023. U.S.Environmental Protection Agency, Athens, GA.
Zukovs, G., J. Kollar and M. Shanahan. 1986.Development of the HAZPRED Model. Proc.Stormwater and Water Quality Model Users GroupMeeting, Orlando, FL. EPA/600/9-86/023, pp. 128-146 (NTIS PB87-117438/AS), U.S. EnvironmentalProtection Agency, Athens, GA.
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Section 9.0
Appendix
Detailed Model Descriptions
UrbanSWMMSTORM
DR3M-QUAL
Non-UrbanEPA Screening Procedures
AGNPSANSWERS
CREAMS/GLEAMSHSPFPRZM
SWRRBUTM-TOX
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Nonpoint Source Model Review
1. Name of Method
Storm Water Management Model (SWMM)
2. Type of Method
Surface Water Model: Simple Approachxx Surface Water Model: Refined Approachxx Soil (Groundwater) Model: Simple Approach
Soil (Groundwater) Model: Refined Approach
3. Purpose/Scope
Purpose: Predict rainfall/runoff/quality processes in urban and other areas. Predict hydrographs and polluto-graphs (concentration vs. time) in
xx runoff watersxx surface watersxx ground waters
Source/Release Types:
xx Continuous xx Intermittentxx Single xx Multiple xx Diffuse
Level of Application:
xx Screening xx Intermediate xx Detailed
Types of Chemicals:
xx Conventional xx Organic xx Metals
Unique Features:
xx Addresses degradation productsxx Integral database/database managerxx Integral uncertainty analysis capabilities
Interactive input/execution manager
4. Level of Effort
System setup: xx mandays xx manweeks manmonthsAssessments: mandays xx manweeks xx manmonths
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(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
The Storm Water Management Model (SWMM) consistsof several modules or ?blocks? designed to simulatemost quantity and quality processes in the urbanhydrologic cycle. Storm sewers, combined sewers,and natural drainage systems can be simulated. Forgeneration of hydrographs, the Runoff Blocksimulates the rainfall-runoff process using a nonlinearreservoir approach, with an option for snowmeltsimulation. Groundwater and unsaturated zone flowand outflow are simulated using a simple lumpedstorage scheme. A water balance is maintainedbetween storms, and the entire model may be used forboth continuous and single event simulation. Flowrouting is accomplished in order of increasingcomplexity in the Runoff Block (nonlinear reservoir),Transport Block (kinematic wave) and/or ExtranBlock (complete dynamic equations). The ExtranBlock is the most comprehensive simulation programavailable in the public domain for a drainage systemdomain and is capable of simulating backwater,surcharging, looped sewer connections and a varietyof hydraulic structures and appurtenances. TheStorage/Treatment Block may be used for storage-indication flow routing. Output from all blocksincludes both flow and stage hydrographs.
Quality processes in the Runoff Block includegeneration of surface runoff constituent loads througha variety of options: 1) build-up of constituents duringdry weather and wash-off during wet weather, 2)? rating curve? approach in which load is proportionalto flow rate to a power, 3) constant concentration(including precipitation loads), and/or 4) UniversalSoil Loss Equation. Removal of ?built-up? loads canoccur by street cleaning during dry weather, andspecial options are available for snow. One constituentcan be taken as a fraction (?potency factor? ) ofanother to simulate adsorption onto solids, forexample. The concentration of groundwater outflowmay only be treated as a constant. Routing andfirst-order decay may be simulated in the Runoff andTransport Blocks. (Extran includes no qualitysimulation.) The Transport Block may also be used tosimulate scour and deposition within the sewersystem based Shield's criterion for initiation ofmotion, and generation of dry-weather flow andquality. The Storage/Treatment Block simulatesremoval in storage/treatment devices by 1) first-orderdecay coupled with complete mixing or plug flow,2) removal functions (e.g., solids deposition as afunction of detention time), or 3) sedimentation
dynamics. Residuals (e.g., sludge) are accounted for.Any constituent may be simulated, but sedimentprocesses, e.g., scour and deposition, are generallyweak with the exception of the Storage/TreatmentBlock.
The blocks can be run independently or in anysequence. Additional blocks are available forstatistical analysis of the output time series (StatisticsBlock), input and manipulation of precipitation,evaporation, and temperature time series (Rain andTemp Blocks), line printer graphics (Graph Block),and output time series manipulation (Combine Block).
6. Data Needs/Availability
SWMM can be run in a very simple configuration,e.g., a single subcatchment and no drainage network,or in a very detailed configuration, e.g., manysubcatchments and channels and pipes. The volumeof data varies accordingly, but at a minimum willrequire information on area, imperviousness, slope,roughness, depression storage and infiltrationcharacteristics at the desired level of detail.Channel/pipe data include shapes, dimensions,slopes or invert elevations, roughnesses, etc. Quantitydata are usually available from the urban municipalityin the form of contour maps and drainage plans.
If quality is generated in the Runoff Block using abuild-up/wash-off formulation, then build-upcoefficients are needed for alternative build-upformulations (i.e., linear, power, exponential orMichaelis-Menton) as well as for wash-off equations.Due to lack of data availability for these conceptualmechanisms, users often resort to rating curves thatmay be more readily derived from measurements, orto constant concentrations.
Precipitation input can be in the form of hyetographsfor individual storm events, or long-term hourly or15-minute precipitation (or arbitrary time interval)records from the National Climatic Data Center.Measured hydrographs and pollutographs (or stormevent loads or event mean concentrations) arerequired for calibration and verification. Quantityresults are often reasonably good with little or no cali-bration, whereas quality results need local,site-specific measurements to ensure accuratepredictions. Since quality measurements areexpensive and sparse, reliance is often placed ongeneralized data from other sites, e.g., EPANationwide Urban Runoff Program (NURP) data,
52
from which reasonable comparative assessments mayusually be made. However, if SWMM (or any urbannonpoint source model) is used to drive a receivingwater quality model, local, site-specific qualitymeasurements are normally required.
7. Output of the Assessment
SWMM produces a time history of flow, stage andconstituent concentration at any point in thewatershed. Seasonal and annual summaries are alsoproduced, along with continuity checks and othersummary output. The Statistics Block may be used forstorm event separation and frequency analysis of anyof the time series. Somewhat crude (by today'sstandards) line printer graphics are available forhydrograph and pollutograph plots, includingcomparison with measured data.
8. Limitations
The simulation methods used in various blocks areextensively described in the SWMM documentationand need to conceptually represent the prototypewatershed for a satisfactory simulation. Experiencehas shown SWMM quantity simulations to comparefavorably with measurements and with otherconventional hydrologic methods, e.g., unithydrographs. Quality simulation is especially weak inrepresentation of the true physical, chemical andbiological processes that occur in nature and is often acalibration or curve-fitting exercise. This accounts forthe tendency for users to bypass build-up/wash-offformulations in favor of constant concentrations orrating curves that may be more easily calibrated.Perhaps the weakest component of the model issimulation of solids transport, although it should beborne in mind that this is difficult to do in any model.The microcomputer version of the program is notespecially ?user friendly? and lacks good graphicsroutines. However, on-screen messages are providedduring program execution to inform the user of thecurrent program status.
9. Hardware/Software Requirements
SWMM is written in ANSI standard Fortran 77.Executable code prepared using the Ryan-McFarlandFortran compiler is distributed for IBMXT/AT-compatibles. A hard disk is required and amath co-processor is usually required (see Contactsection, below). No special peripherals other than a
printer are required. Over 200 sample input files arealso distributed, along with the Fortran source code.The program is also maintained for DEC/VAXsystems; however, the user must perform his/herown compilation from the Fortran source code. Inputfiles must be prepared using an editor capable ofproducing an ASCII file as output. No automatic ormenu-driven input routine is available. The programcan be obtained in either floppy disk format forMS-DOS applications or on a 9-track magnetic tape(from EPA only) with installation instructions for theDEC/VAX VMS environment. SWMM has beeninstalled on many computers world-wide with no orminor modifications.
10. Experience
The original version of SWMM was developed in1969-71 by a consortium of Metcalf and Eddy, Inc.,Water Resources Engineers, Inc. (now a part of Camp,Dresser and McKee, Inc.), and the University ofFlorida (Metcalf and Eddy et al., 1971). The programwas maintained intermittently for several years at theUniversity of Florida under the sponsorship first ofthe EPA Storm and Combined Sewer Branch(Cincinnati) and later by the EPA Center for ExposureAssessment Modeling (Athens, GA). Limited (un-sponsored) maintenance and development workcontinues at the University of Florida. Continuingupdates to the Extran Block have been placed in thepublic domain by Camp, Dresser and McKee, Inc. andincorporated into the model. The most current versionof the model is Version 4 (Huber and Dickinson, 1988;Roesner et al., 1988) following earlier Version 2(Heaney et al., 1975; Huber et al., 1975) and Version 3(Huber et al., 1981; Roesner et al., 1981). A SWMMbibliography (Huber et al., 1985) contains over 200SWMM-related references, including many referencesto case studies. The model is often referenced in texts(e.g., Viessman et al., 1989) and has been used inapplications ranging from routine drainage design tohighly complex hydraulic routing and analysis tolarge nonpoint and point source pollution abatementstudies, e.g., Boston, Detroit, Washington DC.
Maintaining SWMM as a non-proprietary model andin the public domain has produced massive userfeedback to the model developers and led to manyimprovements (Huber, 1989). This has been greatlyfacilitated by approximately semi-annual meetings ofthe Storm and Water Quality Model Users Group andpublication of proceedings by EPA. The model hasachieved the status of a ? standard of comparison? for
53
modelers wishing to develop new and improvedtechniques.
SWMM has been applied to urban hydrologicquantity and quality problems in many locationsworld-wide, including probably over 100 locations inthe U.S. and Canada. Applications in major U.S. citiesinclude Albuquerque, Atlanta, Boston, Chicago,Cincinnati, Denver, Detroit, Hartford, Jacksonville,New Haven, New Orleans, Newark, New York,Philadelphia, Providence, Rochester, San Jose, SanFrancisco, Seattle, Syracuse, Tallahassee, Tampa,Washington DC.
Because SWMM is in the public domain, portions ofthe model have been adapted for related modelingpurposes. A model similar to SWMM in many wayswas developed for the Federal HighwayAdministration (Dever et al., 1981, 1983). It containscomponents of the Runoff and Extran Blocks alongwith specialized hydraulic simulation capabilities forhighway drainage. Because SWMM contains muchmore versatile water quality routines, the FHWAmodel will not be discussed further here; however,some of its characteristics are compared to those ofother models by Huber (1985).
11. Validation/Review
The program has been calibrated and verified onmany independent data sets, and the modelalgorithms have been validated through extensiveoutside analysis and review during the 20 years ofmodel history. Several comparisons of SWMM andother models have been conducted (e.g., Heeps andMein, 1974; Brandstetter, 1977; Huber and Heaney,1982; Huber, 1985; Water Pollution ControlFederation, 1989). In 1982 the U.S. Office ofTechnology Assessment said that its ? reliability andwidespread availability have made SWMM the mostwidely used model of its type in the United States andCanada, and have been important in increasing theuse of models by engineers and planners. The SWMMUser's Group [now the Storm and Water QualityModel Users Group, sponsored by the EPA CEAM]has been instrumental in achieving the widespreaddissemination and acceptance enjoyed by thisimportant modeling tool.? (Office of TechnologyAssessment, 1982).
A final caveat about water quality predictions is stillin order: neither SWMM or any other urban nonpointmodel can predict accurate concentrations and loads
in urban runoff without local, site-specific data. Suchdata would likely be required in order to drive areceiving water quality model, for example. However,relative values and comparison of alternatives canusually still be studied from approximate qualitypredictions.
12. Contact
SWMM is available from the EPA Center for ExposureAssessment Modeling (CEAM) at no charge. It can bedownloaded from the CEAM electronic bulletinboard, from Internet node earth1.epa.gov viaanonymous ftp, or obtained on floppy disks (on twohigh-density disks). The CEAM also maintains theFortran source code for a DEC/VAX VMS version.The same program is also available for $50 from theDepartment of Civil Engineering, Oregon StateUniversity. The Oregon and CEAM versions for PCsrequire a math co-processor. For further informationat the CEAM, contact
Model Distribution CoordinatorAthens Environmental Research Laboratory
U.S. Environmental Protection Agency960 College Station Road
Athens, Georgia 30605(706) 546-3549
Documentation (Huber et al., 1988; Roesner et al.,1988) can be obtained from the NTIS or from OregonState University:
Dr. Wayne C. HuberDept. of Civil Engineering
Oregon State University Apperson Hall 202Corvallis, OR 97331-2302
(503) 737-4934
13. References
Brandstetter, A. 1976. Assessment of MathematicalModels for Storm and Combined SewerManagement, EPA-600/2-76-175a (NTISPB-259597), Environmental Protection Agency,Cincinnati, OH.
Cunningham, B.A. and W.C. Huber. 1987. Economicand Predictive Reliability Implications ofStormwater Design Methodologies, Publication No.98, Florida Water Resources Research Center,University of Florida, Gainesville, 146 pp.
54
Dever, R.J., Jr., L.A. Roesner and J.A. Aldrich. 1983.Urban Highway Storm Drainage Model Vol. 4,Surface Runoff Program User's Manual andDocumentation, FHWA/RD-83/044, FederalHighway Administration, Washington, DC.
Dever, R.J., Jr., L.A. Roesner, and D-C., Woo. 1981.?Development and Application of a DynamicUrban Highway Drainage Model,? UrbanStormwater Hydraulics and Hydrology, Proc.Second International Conference on Urban StormDrainage, Urbana, IL, Water ResourcesPublications, Littleton, CO, pp. 229-235.
Heaney, J.P., W.C. Huber, H. Sheikh, M.A. Medina,J.R. Doyle, W.A. Peltz and J.E. Darling. 1975. UrbanStormwater Management Modeling andDecision-Making, EPA-670/2-75-022 (NTISPB-242290), Environmental Protection Agency,Cincinnati, OH, 186 pp.
Heeps, D.P. and R.G. Mein. 1974. ? Independent Com-parison of Three Urban Runoff Models,? J.Hydraulics Division, Proc. ASCE,100(HY7):995-1009.
Huber, W.C. 1986. ?Deterministic Modeling of UrbanRunoff Quality.? In: Urban Runoff Pollution, Pro-ceedings of the NATO Advanced ResearchWorkshop on Urban Runoff Pollution, Montpellier,France, August 1985, H.C. Torno, J. Marsalek, andM. Desbordes, eds., Springer-Verlag, New York,Series G: Ecological Sciences 10:167-242.
Huber, W.C. 1989. ?User Feedback on Public DomainSoftware: SWMM Case Study? In: Proc. SixthConference on Computing in Civil Engineering,Atlanta, Georgia, ASCE, New York.
Huber, W.C. and R.E. Dickinson. 1988. Storm WaterManagement Model Version 4, User's Manual,EPA/600/3-88/001a (NTIS PB88-236641/AS),Environmental Protection Agency, Athens, GA, 569pp.
Huber, W.C. and J.P. Heaney. 1982. ?AnalyzingResiduals Discharge and Generation from Urbanand Non-Urban Land Surfaces,? Chapter 3, pp.121-243, In: Analyzing Natural Systems, Analysis forRegional Residuals?Environmental QualityManagement, Basta, D.J., and B.T. Bower, eds.,Resources for the Future, Washington, D.C., TheJohns Hopkins University Press, Baltimore, MD,(also available as EPA-600/3-83-046, NTIS
PB83-223321).
Huber, W.C., J.P. Heaney and B.A. Cunningham. 1985.Storm Water Management Model (SWMM) Bibliog-raphy, EPA/600/3-85/077 (NTIS PB86-136041/AS),Environmental Protection Agency, Athens, GA, 58pp.
Huber, W.C., J.P. Heaney, M.A Medina, W.A. Peltz, H.Sheikh and G.F. Smith. 1975. Storm WaterManagement Model User's Manual, Version II,EPA-670/2-75-017 (NTIS PB-257809), Environ-mental Protection Agency, Cincinnati, OH, 351 pp.
Huber, W.C., J.P. Heaney, S.J. Nix, R.E. Dickinson andD.J. Polmann. 1981. Storm Water ManagementModel User's Manual, Version III,EPA-600/2-84-109a (NTIS PB84-198423), Environ-mental Protection Agency, Cincinnati, OH, 531 pp.
Metcalf and Eddy, University of Florida, and WaterResources Engineers. 1971. Storm WaterManagement Model, Vol. I, Final Report,11024DOC07/71 (NTIS PB-203289)EPA WaterPollution Control Research Series Washington,D.C., 352 pp.
Office of Technology Assessment. 1982. Use of Modelsfor Water Resources Management, Planning, andPolicy, U.S. Govt. Printing Office, Washington, DC.
Roesner, L.A., R.P. Shubinski and J.A. Aldrich. 1981.Storm Water Management Model User's Manual,Version III: Addendum I, EXTRAN,EPA-600/2-84-109b (NTIS PB84-198431), Environ-mental Protection Agency, Athens, GA.
Roesner, L.A., J.A. Aldrich and R.E. Dickinson. 1988.Storm Water Management Model User's Manual,Version 4, EXTRAN Addendum,EPA/600/3-88/001b (NTIS PB88-236658/AS),Environmental Protection Agency, Athens, GA.
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56
Nonpoint Source Model Review
1. Name of Method
Storage, Treatment, Overflow, Runoff Model (STORM)
2. Type of Method
xx Surface Water Model: Simple Approach Surface Water Model: Refined Approach Soil (Groundwater) Model: Simple Approach Soil (Groundwater) Model: Refined Approach
3. Purpose/Scope
Purpose: Predict rainfall/runoff/quality processes in urban areas. Predict hydrographs and pollutographs(concentration vs. time) in
xx runoff waters surface waters ground waters
Source/Release Types:
xx Continuous Intermittent Single Multiple xx Diffuse
Level of Application:
xx Screening Intermediate Detailed
Types of Chemicals:
xx Conventional Organic Metals
Unique Features:
Addresses degradation products Integral database/database manager
xx Integral uncertainty analysis capabilities Interactive input/execution manager
4. Level of Effort
System setup: xx mandays xx manweeks manmonthsAssessments: mandays xx manweeks xx manmonths
57
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
58
5. Description of Method/Techniques
The Storage, Treatment, Overflow, Runoff Model(STORM) contains simplified hydrologic and waterquality routines for continuous simulation in urbanareas. Hourly runoff depths are computed by meansof an area-weighted runoff coefficient for the perviousand impervious portions, with recovery of depressionstorage between events. Alternatively, the SCSmethod can be used to generate hourly runoffvolumes. There is no flow routing as such. Runoffpasses through a treatment device up to its capacityand is otherwise diverted to a storage unit. Iftreatment is at capacity and the storage is full, an?overflow? occurs to the receiving water. This schemeresults from the origins of the model for simulation ofcombined sewer overflows. At the end of the storm,remaining storage is routed through treatment. Theprogram is driven by hourly precipitation recordsobtained from the National Climatic Data Center, andsnowmelt can be simulated using the degree-daymethod. Dry-weather flows can also be simulated;these provide a baseflow and are mixed withstormwater runoff during a storm event.
Linear build-up and first-order exponential wash-offis used to simulate concentrations of up to sixpre-specified pollutants (suspended solids, settleablesolids, BOD, total coliforms, ortho-phosphate, andnitrogen). Because build-up and wash-off parametersare adjustable, other conservative pollutants could besimulated under the guise of the above names.Erosion may be simulated using the Universal SoilLoss Equation. Runoff is mixed with dry-weatherflow, if any, and transported without quality routingor decay to the treatment device. Flows passingthrough treatment are not included as loads toreceiving waters (i.e., treatment releases are handledas if there is 100% removal). However, a summary ismaintained of concentrations and loads that bypassthe storage/treatment option and overflow to thereceiving water. No treatment occurs in the storagedevice.
6. Data Needs/Availability
Data needs are somewhat less for STORM than forcomparable continuous simulation models because ofits simple hydrologic and water quality routines.Runoff coefficients may be estimated from standardhandbooks and textbooks, and SCS parameters arewidely available if the soil types are known. Build-upand wash-off parameters are less flexible than in
models such as SWMM, HSPF or DR3M-QUAL andno more easy to estimate. Calibration is advisable butsomewhat awkward because the model is primarilyset up to run in only a continuous mode. However,since STORM is usually run only in a screening orplanning mode, comparative evaluations can usuallybe made without calibration.
7. Output of the Assessment
Output includes storm event summaries of runoffvolume, concentrations and loads plus summaries ofstorage and treatment utilization and total overflowloads and concentrations. Hourly hydrographs andpollutographs (concentration vs. time) may becomputed but rarely are because there is no way to dothis for only brief periods during a long, continuoussimulation. Useful statistical summaries on an annualand total simulation period basis that enable an esti-mation of ?percent control,? i.e., percentage of runoffpassing through storage and also the number ofoverflows, both as a function of the treatment rate andstorage capacity. The storage-treatment combinationcan then be optimized. The utilization of storage isalso summarized, which helps in defining theduration of critical events, e.g., time required betweenevents for complete drainage of the storage.
8. Limitations
STORM's hydrologic routines (runoff coefficient, SCS,no routing) are the simplest of any simulation modelconsidered. Although they lead to minimal datarequirements, they also lead to less flexibility inmatching observed hydrographs. Similarly, STORMuses the quality routines embodied in the originalSWMM program (Metcalf and Eddy et al., 1971) withvery few modifications. These have been shown to berelatively inflexible in matching observed polluto-graphs and have been updated in SWMM and othermodels. Only hourly precipitation inputs are possible,making it difficult to work with more recentcontinuous records of 15-minute precipitation data orarbitrary input hyetographs, e.g., measured rainfalland runoff data. Lack of an agency-supportedmicrocomputer version currently hampers themodel's use.
9. Hardware/Software Requirements
STORM is written in Fortran IV and must becompiled by the user on a mainframe. It is available
59
only on a 9-track magnetic tape and has been installedon IBM and CDC systems, among others. Althoughindividual users have prepared versions for IBMPC/AT-compatibles, the model developer does notsell or support such a version.
10. Experience
STORM represents the first significant use ofcontinuous simulation, especially quality applications,in the urban setting. (The Stanford Watershed Model,incorporated into HSPF, was the first continuousmodel.) The model was developed by the ArmyCorps of Engineers, Hydrologic Engineering Center(HEC) originally for application to the San Franciscomaster drainage plan (Roesner et al., 1974; HEC, 1977)for abatement of combined sewer overflows (CSOs)into San Francisco Bay (McPherson, 1974). Sixty-twoyears of hourly rainfall data were analyzed withSTORM to provide optimal combinations of storageand treatment for the master plan. The HEC alsoprovides additional guidelines for model application(Abbott, 1977).
The support of the HEC, renowned for the suite ofhydrologic models, led to extensive use of STORM inthe late 70s and early 80s, but the lack of HEC updatesin recent years has caused a decline in model use.Nonetheless, many applications may be found,including Heaney et al. (1977) for a nationwideassessment, Shubinski et al. (1977) for the Detroit area,and Najarian et al. (1986) in New Jersey. A simplifiedreceiving water quality model was developed forstreams for use with the STORM model (Medina,1979), although Medina's model could also be drivenby other continuous simulation models.
A very similar model to STORM was developed byLager et al. (1976) for application in San Francisco andRochester, known as ?Simplified SWMM.? This modelis not currently available, and STORM is essentially asubstitute.
11. Validation/Review
STORM has been used in many applications althoughseldom compared against measured rainfall-runoffdata; one such comparison is by Abbott (1978) inwhich model predictions compared favorably withhourly runoff values. The model's many applicationsby diverse users serve as a form of validation.
12. Contact
In 1989, the distribution of the most popular of theHEC programs, e.g., HEC-1 and HEC-2 and someothers, was transferred to selected private vendors.Direct HEC support for these programs is limited toCorps of Engineers or other federal agencies.However, STORM is still available direct ly only fromthe HEC for $200 on a 9-track magnetic tape. Nomicrocomputer version is available. Only the FortranIV source code is distributed; the user must performthe compilation. Contact
The Hydrologic Engineering CenterCorps of Engineers609 Second Street
Davis, California 95616
13. References
Abbott, J. 1977. Guidelines for Calibration andApplication of STORM, Training Document No. 8,Hydrologic Engineering Center, Corps ofEngineers, Davis, CA.
Abbott, J. 1978. Testing of Several Runoff Models onan Urban Watershed, ASCE Urban WaterResources Research Program, TechnicalMemorandum No. 34, ASCE, New York.
Heaney, J.P., W.C. Huber, M.A. Medina, M.P. Murphy,S.J. Nix and S.M. Hasan. 1977. NationwideEvaluation of Combined Sewer Overflows andUrban Stormwater Discharges, Vol. II: CostAssessment and Impacts, EPA-600/2-77-064b(NTIS PB-242290), Environmental ProtectionAgency, Cincinnati, OH.
Hydrologic Engineering Center. 1977. Storage,Treatment, Overflow, Runoff Model, STORM,User's Manual, Generalized Computer Program723-S8-L7520, Corps of Engineers, Davis, CA.
McPherson, M.B. 1974. ? Innovation: A Case Study,?ASCE Urban Water Resources Research Program,Technical Memorandum No. 21 (NTIS PB-232166),ASCE, New York.
Medina, M.A. 1979. Level III: Receiving Water QualityModeling for Urban Stormwater Management,EPA-600/2-79-100 (NTIS PB80-134406), Environ-mental Protection Agency, Cincinnati, OH.
60
Metcalf and Eddy, Inc., University of Florida, WaterResources Engineers, Inc. 1971. Storm WaterManagement Model, Volume I: Final Report,Report 11024DOC07/71 (NTIS PB-203289),Environmental Protection Agency, Washington,DC.
Najarian, T.O, T.T. Griffin and V.K. Gunawardana.1986. ?Development Impacts on Water Quality: ACase Study? Journal Water Resources Planning andManagement, ASCE, 112(1):20-35.
Roesner, L.A., H.M. Nichandros, R.P. Shubinski, A.D.Feldman, J.W. Abbott and A.O. Friedland. 1974. AModel for Evaluating Runoff-Quality inMetropolitan Master Planning, ASCE Urban WaterResources Research Program, TechnicalMemorandum No. 23 (NTIS PB-234312), ASCE,New York.
Shubinski, R.P., A.J. Knepp and C.R. Bristol. 1977.Computer Program Documentation for theContinuous Storm Runoff Model SEM-STORM,Report to the Southeast Michigan Council ofGovernments, Detroit, MI.
61
Nonpoint Source Model Review
1. Name of Method
Distributed Routing Rainfall Runoff Model?Quality DR3M-QUAL
2. Type of Method
Surface Water Model: Simple Approachxx Surface Water Model: Refined Approach
Soil (Groundwater) Model: Simple Approach Soil (Groundwater) Model: Refined Approach
3. Purpose/Scope
Purpose: Predict rainfall/runoff/quality processes in urban and other areas. Predict hydrographs andpollutographs (concentration vs. time) in
xx runoff watersxx surface waters
ground waters
Source/Release Types:
xx Continuous xx Intermittent Single Multiple xx Diffuse
Level of Application:
Screening xx Intermediate xx Detailed
Types of Chemicals:
xx Conventional xx Organic xx Metals
Unique Features:
xx Addresses degradation products Integral database/database manager Integral uncertainty analysis capabilities Interactive input/execution manager
4. Level of Effort
System setup: xx mandays xx manweeks manmonthsAssessments: mandays xx manweeks xx manmonths
62
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
63
5. Description of the Method/Techniques
The Distributed Routing Rainfall RunoffModel?Quality (DR3M-QUAL) incorporates waterquality routines into an updated version of an earlierU.S. Geological Survey (USGS) urban hydrologicmodel (Dawdy et al., 1972). Runoff is generated fromrainfall using the kinematic wave method overmultiple subcatchments and routed through drainagepathways by the same technique. Storage-indicationrouting is available for storage basins. The model canbe run over any time period and is sometimes used tosimulate a group of storms while bypassingsimulation of the intervening dry periods (although amoisture balance is maintained). A built-inoptimization routine aids in estimation of quantityparameters.
Quality is simulated for arbitrary parameters usingexponential build-up functions plus wash-offfunctions determined from experience with modelcalibration. Considerable guidance is provided forparameter estimation. Removal of built-up solids canoccur during dry weather by street cleaning. Erosionis simulated using empirical equations relatingsediment yield to runoff volume and peak. Someguidance is provided for the erosion parameters usingrelationships based on the Universal Soil LossEquation. Concentrations of other constituents can betaken as a fraction (?potency factors? ) of sedimentconcentration. Precipitation can contribute a constantconcentration.
Quality routing through the drainage network is doneby a Lagrangian scheme to simulate plug flow and nodecay. Plug flow routing is also performed in storagebasins, with settling based on sedimentation theoryand dependent on a particle size distribution.
6. Data Needs/Availability
Data needs depend on the degree of schematization.Quantity parameters for a subcatchment include area,imperviousness, length, slope, roughness andinfiltration parameters. Channels are characterized bytrapezoidal or circular dimensions and kinematicwave parameters. Storage basins requirestage-area-discharge relationships. Qualityparameters include build-up and wash-offcoefficients. Rainfall input can be for single ormultiple storms.
Quantity data are similar in form to those required by
other urban hydrologic models and may be derivedfrom contour maps and drainage plans available frommunicipalities. Build-up and wash-off parameters arevery difficult to estimate a priori and require local,site-specific quality measurements for calibration ifaccurate quality predictions are needed, e.g., to drivea receiving water quality model.
7. Output of the Assessment
DR3M-QUAL produces a time history of runoffhydrographs and quality pollutographs(concentration or load vs. time) at any location in thedrainage system. Summaries for storm events areprovided as well as line printer graphics forhydrographs and pollutographs.
8. Limitations
The kinematic wave is perhaps as good a conceptualmodel as exists for overland flow but approximationsare always inherent in its application, as for anyconceptual model. The quantity model can beexpected to perform reasonably well with minimalcalibration. No interaction among quality parametersexists (other than the ability to treat one pollutant as afraction of sediment concentration). Except for thesedimentation algorithm within storage units,simulation of sediment transport processes is weak, aswith virtually all other models of this type. Generally,quality predictions must be calibrated if accurateconcentrations and loads are required, e.g., to drive areceiving water quality model. On the other hand,relative comparisons can be made using generalizedU.S. data for calibration purposes.
9. Hardware/Software Requirements
The program is written in Fortran 77 for IBM or Primemainframes. Source code is provided on a 9-trackmagnetic tape for compilation and installation on theuser's computer.
10. Experience
The basis for the hydrologic components ofDR3M-QUAL is the earlier modeling work of Dawdyet al. (1972) that was updated first for the quantityportion of the model (Dawdy et al., 1978; Alley andSmith, 1982a) and then for additional quality routines(Alley and Smith, 1982b). Much of the emphasis onthe form and calibration of build-up and wash-off
64
parameters is based on research done by Alley et al.(1980), Alley (1981), and Alley and Smith (1981). Theprogram has received extensive internal reviewwithin the USGS and has been applied to their urbanmodeling studies in South Florida (Doyle and Miller,1980), Rochester (Kappel et al., 1985; Zarriello, 1988),Anchorage (Brabets, 1986), Denver (Lindner-Lunsfordand Ellis, 1987), and Fresno (Guay and Smith, 1988). Asummary of experience with the model is given byAlley (1986).
11. Validation/Review
The program has undergone the extensive internalpeer review process of the USGS and has been testedand compared with field data on at least 37catchments by various individuals (Alley, 1986). It hasprimarily been used by persons within the USGSalthough it is freely available to anyone.
12. Contact
The Fortran 77 source code and documentation areavailable on a 9-track magnetic tape (to be suppliedby the user) from the USGS National Center:
Ms. Kate FlynnU.S. Geological Survey
410 National CenterReston, Virginia 22092
(703) 648-5313
Minimal support is available from the programauthors (Alley and Smith).
13. References
Alley, W.M. 1981. ?Estimation of Impervious-AreaWashoff Parameters,? Water Resources Research,17(4):1161-1166.
Alley, W.M. 1986. ?Summary of Experience with theDistributed Routing Rainfall-Runoff Model(DR3M).? In: Urban Drainage Modeling, C.Maksimovic and M. Radojkovic, eds., Proc. ofInternational Symp. on Comparison of UrbanDrainage Models with Real Catchment Data,Dubrovnik, Yugoslavia, Pergamon Press, NewYork, pp. 403-415.
Alley, W.M., F.W. Ellis and R.C. Sutherland. 1980.?Toward A More Deterministic Urban Runoff
Quality Model,? Proc. of the InternationalSymposium on Urban Runoff, University ofKentucky, Lexington, pp. 171-182.
Alley, W.M. and P.E. Smith. 1981. ?Estimation ofAccumulation Parameters for Urban RunoffQuality Modeling,? Water Resources Research,17(6):1657-1664.
Alley, W.M. and P.E. Smith. 1982a. DistributedRouting Rainfall-Runoff Model?Version II, USGSOpen File Report 82-344, Reston, VA.
Alley, W.M. and P.E. Smith. 1982b. Multi-Event UrbanRunoff Quality Model, USGS Open File Report82-764, Reston, VA.
Brabets, T.P. 1987. Quantity and Quality of UrbanRunoff from the Chester Creek Basin Anchorage,Alaska, USGS Water-Resources InvestigationsReport 86-4312, Anchorage, AK.
Dawdy, D.R., R.W. Lichty and J.M. Bergmann. 1972. ARainfall-Runoff Simulation Model for Estimation ofFlood Peaks for Small Drainage Basins, USGSProfessional Paper 506-B, Reston, VA.
Dawdy, D.R., J.C. Schaake, Jr. and W.M. Alley. 1972.User's Guide for Distributed RoutingRainfall-Runoff Model, USGS Water ResourcesInvestigations 78-90, Gulf Coast HydroscienceCenter, NSTL Station, MS.
Doyle, W.H., Jr. and J.E. Miller. 1980. Calibration of aDistributed Routing Rainfall-Runoff Model at FourUrban Sites Near Miami, Florida, USGS WaterResources Investigations 80-1, Gulf CoastHydroscience Center, NSTL Station, MS.
Guay, J.R. and P.E. Smith. 1988. Simulation ofQuantity and Quality of Storm Runoff for UrbanCatchments in Fresno, California, USGSWater-Resources Investigations Report 88-4125,Sacramento, CA.
Kappel, W.M., R.M. Yager and P.J. Zarriello. 1986.Quantity and Quality of Urban Storm Runoff in theIrondequoit Creek Basin near Rochester, New York,Part 2. Quality of Storm Runoff and AtmosphericDeposition, Rainfall-Runoff-Quality Modeling, andPotential of Wetlands for Sediment and NutrientRetention, USGS Water-Resources InvestigationsReport 85-4113, Ithaca, New York.
65
Lindner-Lunsford, J.B. and S.R. Ellis. 1987.Comparison of Conceptually Based and RegressionRainfall-Runoff Models, Denver Metropolitan Area,Colorado, and Potential Applications in UrbanAreas, USGS Water-Resources InvestigationsReport 87-4104, Denver, CO.
Zarriello, P.J. 1988. ?Simulated Water-Quality Changesin Detention Basins.? In Design of Urban RunoffQuality Controls, L.A. Roesner, B. Urbonas andM.B. Sonnen, eds., Proc. of Engineering FoundationConference, Potosi, MO, ASCE, New York, pp.268-277.
66
Nonpoint Source Model Review
1. Name of the Method
EPA Screening Procedures
2. Type of Method
xxx Surface Water Model: Simple Approach Surface Water Model: Refined Approach Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff watersxxx surface waters
ground waters
Source/Release Types:
Continuous Intermittent Single xxx Multiple xxx Diffuse
Level of Application:
xxx Screening Intermediate Detailed
Type of Chemicals:
xxx Conventional xxx Organic xxx Metals
Unique Features:
Addresses degradation products Integral Database/Database manager Integral Uncertainty Analysis Capabilities Interactive Input/Execution Manager
67
4. Level of Effort
System setup: xx mandays manweeks manmonths manyearAssessments: xx mandays xx manweeks manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
68
5. Description of the Method/Techniques
EPA Screening Procedures (Mills et al., 1982, 1985) are arevision and expansion of water quality assessmentprocedures initially developed for nondesignated 208areas (Zison et al., 1977). The procedures have beenexpanded and revised to include consideration of theaccumulation, transport, and fate of toxic chemicals,in addition to conventional pollutants included in theearliest version. The manual includes a separatechapter describing calculation procedures forestimating NPS loads for urban and nonurban landareas in addition to chapters on procedures for riversand streams, impoundments, and estuaries. A usefuloverview chapter on the aquatic fate processes oftoxic organisms is also included.
The procedures for NPS load assessments describedin the manual are essentially a compilation andintegration of estimation techniques developed earlierby Midwest Research Institute (McElroy et al., 1976),Amy et al. (1974), Heany et al. (1976) (i.e., SWMM-Level I), and Haith (1980). However, the presentationof the procedures is well integrated, supplementedwith additional parameter estimation information,and includes sample calculations. The procedures fornonurban areas are derived from MRI loading func-tions for average annual estimates based on UniversalSoil Loss Equation (Wischmeier and Smith, 1978),while the storm event procedures use the modifiedUSLE (Williams, 1975) and the SCS Runoff CurveNumber procedure (Mockus, 1972) for storm runoff.Pollutant concentrations in soils and enrichmentratios are required for estimating pollutant loads;precipitation contributions of nutrients can beincluded. Specific information is included forestimation of salinity loads in irrigation return flows,and storm event pesticide losses are estimatedseparately based on procedures developed by Haith(1980).
Annual and storm event loads from urban areasfollow the procedures included in the SWMM-Level I(Heaney et al., 1976) and Amy et al. (1974)respectively. The basic procedures are relativelystandard for urban areas, calculating pollutant loadsas a function of solids accumulation and washoff.
The EPA Screening Procedures have excellent userdocumentation and guidance, including occasionalworkshops sponsored by the EPA Water QualityModeling Center, Athens, Georgia.
6. Data Needs/Availability
The data requirement is minimal and is also availablefrom the manual when site specific data is lacking.Soil and land use data is required for estimatingrunoff and sediment yield. This data is readilyavailable from USDA-SCS soil survey reports. Tablesfor estimating water quality input parameters fornitrogen, phosphorous, heavy metals, and pesticidesare included in the manual.
7. Output of the Assessment
Output available from the model include predictedstream concentrations of BOD, DO, total N, total P,temperature, and conservative and organic pollutantsby reach; total lake concentrations, organic pollutantseutrophic status, and hypolimnion DO deficit; andestuary concentrations of BOD, DO, total N, total P,and conservative pollutants by reach. As calculationsare done on a hand calculator they can be arrangedaccording to user's convenience.
8. Limitations
It is a very simplified procedure requiring the user toestimate pollutant concentrations. It use is verylimited while evaluating impacts of variousmanagement practices. As calculations are done on ahand calculator, they can be tedious and timeconsuming for complex multi-media use basins.Snowmelt runoff and loadings are ignored.
9. Hardware/Software Requirements
No computer requirement, calculations can be doneby hand calculators.
10. Experience
These procedures have enjoyed wide popularity,partly due to the availability of training workshopssponsored by EPA, and have been applied in anumber of regions, including the Sandusky River innorthern Ohio and the Patuxent, Ware, Chester, andOccuquan basins in the Chesapeake Bay region (Daviset al., 1981; Dean et al., 1981a; and Dean et al., 1981b).
11. Validation/Review
69
12. Contact
For copies of the manual contact
Center for Exposure Assessment ModelingU.S. Environmental Protection Agency
Environmental Research LaboratoryAthens, GA. 30605
(706) 546-3549
13. References
Amy, G., R. Pitt, R. Singh, W.L. Bradford and M.B.LaGraff. 1974. Water Quality ManagementPlanning for Urban Runoff. U.S. EPA, Washington,D.C., EPA 440/9-75-004. (NTIS PB 241 689/AS).
Davis, M.J., M.K. Snyder and J.W. Nebgen. 1981.River Basin Validation of the Water QualityAssessment Methodology for ScreeningNondesignated 208 Areas?Volume I: NonpointSource Load Estimation. U.S. EPA, Athens, GA.
Dean, J.D., W.B. Mills and D.B. Porcella. 1981a. AScreening Methodology for Basin Wide WaterQuality Management. Symposium on Unified RiverBasin Management. R.M. North, L.B. Dwoesky, andD.J. Allee (editors), May 4-7, 1980, Gatlinburg, TN.
Dean, J.D., B. Hudson and W.B. Mills. 1981b. RiverBasin Validation of the MRI Nonpoint Calculatorand Tetra Tech's Nondesignated 208 ScreeningMethodologies, Volume II. Chesapeake-SanduskyNondesignated 208 Screening MethodologyDemonstration. U.S. EPA, Athens, GA.
Haith, D.A. 1980. ?A Mathematical Model forEstimating Pesticide Losses in Runoff? . J. of Env.Qual. 9(3):428-433.
Heaney, J.P., W.C. Huber and S.J. Nix. 1976. StormWater Management Model, Level I, PreliminaryScreening Procedures. EPA-600/2-76-275, U.S.Environmental Protection Agency. Cincinnati, OH.
McElroy, A.D., S.Y. Chiu, J.W. Nebgen, A. Aleti andF.W. Bennett. 1976. Loading Functions forAssessment of Water Pollution from NonpointSources. EPA-600/2-76-151. U.S. EnvironmentalProtection Agency. Washington, D.C.
Mills, W.B., V.H. Colber and J.D. Dean. 1979. Hand-Held Calculator Programs for Analysis of RiverWater Quality Interactions. Tetra Tech Inc.,Lafayette, CA.
Mills, W.B., J.D. Dean, D.B. Porcella, S.A. Gherini,R.J.M. Hudson, W.E. Frick, G.L. Rupp and G.L.Bowie. 1982. Water Quality Assessment: AScreening Procedure for Toxic and ConventionalPollutants. EPA-600/6-82-004a and b. Volumes Iand II. U.S. Environmental Protection Agency.
Mills, W.B., D.B. Porcella, M.J. Ungs, S.A. Gherini,K.V. Summers, L. Mok, G.L. Rupp, G.L. Bowie andD.A. Haith. 1985. Water Quality Assessment: AScreening Procedure for Toxic and ConventionalPollutants in Surface and Ground Water.EPA/600/6-85/002a. U.S. Environmental Pro-tection Agency.
Mockus, J. 1972. ?Estimation of Direct Runoff fromStorm Rainfall.? In: National EngineeringHandbook, Sec. 4, Hydrology. U.S. SoilConservation Service, Washington, D.C.
Williams, J.R. 1975. ?Sediment-Yield Prediction with
70
Universal Soil Loss Equation Using Runoff EnergyFactor.? In: Present and Prospective Technology forPredicting Sediment Yields and Sources. U.S. Dept.of Agriculture. ARS-S-40.
Wischmeier, W.H. and D.D. Smith. 1978. PredictingRainfall-Erosion Losses: A Guide to ConservationPlanning. U.S. Department of Agriculture,Agriculture Research Service.
Zison, S.W., K. Haven and W.B. Mills. 1977. WaterQuality Assessment: A Screening Methodology forNondesignated 208 Areas. EPA-600/6-77-023, U.S.EPA, Athens, GA.
Nonpoint Source Model Review
1. Name of the Method
Agricultural NonPoint Source Pollution Model? (AGNPS)
2. Type of Method
Surface Water Model: Simple Approachxxx Surface Water Model: Refined Approach
Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff watersxxx surface waters
ground waters
Source/Release Types:
Continuous Intermittent Single xxx Multiple xxx Diffuse
Level of Application:
xxx Screening xxx Intermediate xxx Detailed
Type of Chemicals:
xxx Conventional Organic Metals
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Unique Features:
Addresses degradation products Integral Database/Database manager Integral Uncertainty Analysis Capabilities
xxx Interactive Input/Execution Manager
4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
Agricultural Nonpoint Source Pollution Model (AGNPS)was developed by the U.S. Department ofAgriculture?Agriculture Research Service (Young etal., 1986) to obtain uniform and accurate estimates ofrunoff quality with primary emphasis on nutrientsand sediments and to compare the effects of variouspollution control practices that could be incorporatedinto the management of watersheds.
The AGNPS model simulates sediments and nutrientsfrom agricultural watersheds for a single storm eventor for continuous simulation. Watersheds examinedby AGNPS must be divided into square workingareas called cells. Grouping of cells results in theformation of subwatersheds, which can beindividually examined. The output from the modelcan be used to compare the watershed examinedagainst other watersheds to point sources of waterquality problems, and to investigate possible solutionsto these problems.
AGNPS is also capable of handling point sourceinputs from feedlots, waste water treatment plantdischarges, and stream bank and gully erosion (userspecified). In the model, pollutants are routed fromthe top of the watershed to the outlet in a series ofsteps so that flow and water quality at any point inthe watershed may be examined. The Modified Uni-versal Soil Loss Equation is used for predicting soilerosion, and a unit hydrograph approach used for theflow in the watershed. Erosion is predicted in fivedifferent particle sizes namely sand, silt, clay, smallaggregates, and large aggregates.
The pollutant transport portion is subdivided into onepart handling soluble pollutants and another parthandling sediment attached pollutants. The methodsused to predict nitrogen and phosphorus yields fromthe watershed and individual cells were developed byFrere et al. (1980) and are also used in CREAMS(Knisel, 1980). The nitrogen and phosphoruscalculations are performed using relationships be-tween chemical concentration, sediment yield andrunoff volume.
Data needed for the model can be classified into twocategories: watershed data and cell data. Watersheddata includes information applying to the entirewatershed which would include watershed size,number of cells in the watershed, and if running for asingle storm event then the storm intensity. The cell
data includes information on the parameters based onthe land practices in the cell.
Additional model components that are underdevelopment are unsaturated/saturated zoneroutines, economic analysis, and linkage toGeographic Information System.
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6. Data Needs/Availability
The input data needed are extensive however, can beobtained through visual field observations, maps(topographic and soils) and from variouspublications, table and graphs (Young et al. 1986).Meteorologic data consisting of daily rainfall isneeded for hydrology simulation. The model also hasan option to evaluate watershed response to a singlestorm event. Data on soil and land use can beobtained from local USDA-SCS field offices.
7. Output of the Assessment
The model provides estimates of: (1) hydrology, withestimates of both runoff volume and peak runoff rate;(2) sediment, with estimates of upland erosion,channel erosion, and sediment yield; and nutrients(both sediment attached and dissolved), withestimates of pollution loadings to receiving cells. Agraphics option in the program allows the user to plotdifferent variables within the watershed.
8. Limitations
The model does not handle pesticides. The pollutanttransport component needs further field testing.Nutrient transformation and instream processes arenot within model capabilities.
9. Hardware/Software Requirements
The program is written in standard FORTRAN 77 andhas been installed on IBM PC/AT and compatibles. Ahard disk is required for operation of the programand a math co-processor is highly recommended.Executable code prepared with Ryan-McFarlandcompiler and is available only for MS/DOSenvironment. Source code is only available forMS/DOS environment.
10. Experience
The model is being used extensively within UnitedStates to evaluate nonpoint source pollution byvarious government agencies and consultants. Themodel has been used by Shi (1987) to performeconomic assessment of soil erosion and water qualityin Idaho. Setia and Magleby (1987) and Setia et al.(1988) used AGNPS for evaluating the economic effectof nonpoint pollution control alternatives. The model
has also been used by Koelliker and Humbert (1989)for water quality planning. APNPS was used byFrevert and Crowder (1987) to analyze agriculturalnonpoint pollution control options in the St. AlbansBay watershed.
11. Validation/Review
The model has been validated using field data fromagricultural watersheds in Minnesota, Iowa, andNebraska (Young et al., 1986). Lee (1987) validated themodel in an Illinois watershed using the single stormoption of the model. The author found that thesimulated and observed data for runoff volume andsediment yield were well represented when comparedwith observed data.
12. Contact
The copies of the AGNPS program and the manualare available from
Dr. Robert YoungUSDA-ARS
North Central Research LaboratoryMorris, MN 56267
Phone: (612) 589-3411
13. References
Frere, M.H., J.D. Ross and L.J. Lane. 1980. ?TheNutrient Submodel.? In: Knisel, W.G. (ed.). 1980.CREAMS: A Field Scale Model for Chemicals,Runoff, and Erosion from AgriculturalManagement Systems. U.S. Department of Agri-culture. Conservation Research Report No. 26. pp.65-86.
Frevert, K. and B.M. Crowder. 1987. ?Analysis ofagricultural nonpoint pollution control options inthe St. Albans Bay watershed.? Economic Division,ERS, USDA. Staff Report No. AGES870423.
Knisel, W.G. (ed.). 1980. CREAMS: A Field ScaleModel for Chemicals, Runoff, and Erosion fromAgricultural Management Systems. U.S.Department of Agriculture. Conservation ResearchReport No. 26. 640 pp.
Lee, M.T. 1987. ?Verification and Applications of aNonpoint Source Pollution Model.? In: Proceedingsof the National Engineering Hydrology
74
Symposium. ASCE, New York, NY.
Setia, P.P. and R.S. Magleby. 1987. ?An EconomicAnalysis of Agricultural Nonpoint PollutionControl Alternatives.? Jour. of Soil and WaterConservation. 42:427-431.
Setia, P.P., R.S. Magleby and D.G Carvey. 1988.? Illinois Rural Clean Water Project?An EconomicAnalysis.? Resources and Technology Division,ERS, USDA. Staff Report No. AGES830617.
Shi, H.Q. 1987. ? Integrated Economic Assessment ofSoil Erosion and Water Quality in Idaho's TomBeall Watershed.? M.Sc. Thesis. University ofIdaho.
Young, R.A., C.A. Onstad, D.D. Bosch and W.P.Anderson. 1986. Agricultural Nonpoint SourcePollution Model: A Watershed Analysis Tool.Agriculture Research Service, U.S. Department ofAgriculture, Morris, MN.
75
Nonpoint Source Model Review
1. Name of the Method
Areal Nonpoint Source Watershed Environment Response Simulation? (ANSWERS)
2. Type of Method
Surface Water Model: Simple Approachxxx Surface Water Model: Refined Approach
Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach
xxx Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff waters surface waters ground waters
Source/Release Types:
Continuous Intermittent Single Multiple xxx Diffuse
Level of Application:
xxx Screening xxx Intermediate Detailed
Type of Chemicals:
xxx Conventional Organic Metals
Unique Features:
Addresses degradation productsxxx Integral Database/Database manager
Integral Uncertainty Analysis Capabilities Interactive Input/Execution Manager
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4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
Areal Nonpoint Source Watershed Environment ResponseSimulation (ANSWERS) was developed at theAgricultural Engineering Department of PurdueUniversity (Beasley and Huggins, 1981). It is an eventbased, distributed parameter model capable ofpredicting the hydrologic and erosion response ofagricultural watersheds.
Application of ANSWERS requires that the watershedto be subdivided into a grid of square elements. Eachelement must be small enough so that all importantparameter values within its boundaries are uniform.For a practical application element sizes range fromone to four hectares. Within each element the modelsimulates the processes of interception, infiltration,surface storage, surface flow, subsurface drainage,and sediment drainage, and sediment detachment,transport, and deposition. The output from oneelement then becomes a source of input to an adjacentelement.
As the model is based on a modular programstructure it allows easier modification of existingprogram code and/or addition of user suppliedalgorithms. Model parameter values are allowed tovary between elements, thus, any degree of spatialvariability within the watershed is easily represented.
Nutrients (nitrogen and phosphorus) are simulatedusing correlation relationships between chemicalconcentrations, sediment yield and runoff volume. Aresearch version (Amin-Sichani, 1982) of the modeluses ? clay enrichment? information and a verydescriptive phosphorus fate model to predict total,particulate, and soluble phosphorus yields.
6. Data Needs/Availability
Data need comprise of detailed description of thewatershed topography, drainage network, soils, andland use. Most of the data can be obtained fromUSDA-SCS soil surveys, land use and croppingsurveys.
7. Output of the Assessment
The model can evaluate alternative erosion controlmanagement practices for both agricultural land andconstruction sites (Dillaha et al., 1982). Output can beobtained on an element basis or for the entire
watershed in terms of flow and sediment. TheANSWERS program comes with a plotting program.
8. Limitations
For a simulation run of ANSWERS on a largewatershed a mainframe computer is required.However, for smaller watersheds a IBM-PCcompatible version of ANSWERS is available. Themodel is a storm event model and the input data fileis quite complex to prepare. Snowmelt processes orpesticides cannot be simulated by the model. Thewater quality constituents modeled are limited tonitrogen and phosphorous. These constituents arerepresented by relationships between chemicalconcentrations with sediment yield and runoffvolume. No transformation of nitrogen andphosphorus is accounted for in the model.
9. Hardware/Software Requirements
The program is written in standard FORTRAN 77 andhas been installed on IBM PC/AT and compatibles. Ahard disk is required for operation of the programand a math co-processor is highly recommended.Executable code prepared with Ryan-McFarlandcompiler.
10. Experience
The model has been successfully applied in Indianaon an agricultural watershed and a construction siteto evaluate best management practices by Beasley(1986).
11. Validation/Review
Individual components of the model have beenvalidated by the developers.
12. Contact
To obtain copies of the model please write or call Dr.David Beasley at the following address:
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Dr. David Beasley, Professor and HeadDept. of Agricultural Engineering
University of GeorgiaCoastal Plain Experiment Station
P.O. Box 748Tifton, GA 31793
(912) 386-3377
13. References
Amin-Sichani, S. 1982. ?Modeling of PhosphorusTransport in Surface Runoff from AgriculturalWatersheds.? Ph.D. Thesis, Purdue University, W.Lafayette, Indiana, 157 pp.
Beasley, D.B. and L.F. Huggins. 1981. ANSWERSUsers Manual. EPA-905/9-82-001, U.S. EPA, RegionV. Chicago, IL.
Beasley, D.B. 1986. ?Distributed ParameterHydrologic and Water Quality Modeling.? In:Agricultural Nonpoint Source Pollution: ModelSelection and Application. A. Giorgini and F. Zingales(editors). pp. 345-362.
Beasley, D.B. and D.L. Thomas. 1989. Application ofWater Quality Models for Agricultural andForested Watersheds. Southern Cooperative SeriesBulletin No. 338. Agricultural Experiment Station,University of Georgia, Athens, GA.
Dillaha, T.A. III, D.B. Beasley and L.F. Huggins. 1982.?Using the ANSWERS Model to Estimate SedimentYields on Construction Sites.? J. of Soil and WaterCons., 37(2):117-120.
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Nonpoint Source Model Review
1. Name of the Method
Chemicals, Runoff, and Erosion from Agricultural Management Systems? (CREAMS)Groundwater Loading Effects of Agricultural Management Systems? (GLEAMS)
2. Type of Method
Surface Water Model: Simple Approachxxx Surface Water Model: Refined Approach
Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach
xxx Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff waters surface waters
xxx ground waters (vadose and root zone)
Source/Release Types:
Continuous Intermittent Single xxx Multiple xxx Diffuse
Level of Application:
xxx Screening xxx Intermediate xxx Detailed
Type of Chemicals:
xxx Conventional xxx Organic Metals
Unique Features:
xxx Addresses degradation products Integral Database/Database manager Integral Uncertainty Analysis Capabilities Interactive Input/Execution Manager
80
4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
Chemicals, Runoff, and Erosion from AgriculturalManagement Systems (CREAMS) was developed by theU.S. Department of Agriculture?AgriculturalResearch Service (Knisel, 1980; Leonard and Ferreira,1984) for the analysis of agricultural best managementpractices for pollution control. CREAMS is a fieldscale model that uses separate hydrology, erosion, andchemistry submodels connected together by pass files.
Runoff volume, peak flow, infiltration,evapotranspiration, soil water content, andpercolation are computed on a daily basis. If detailedprecipitation data are available then infiltration iscalculated at histogram breakpoints. Daily erosionand sediment yield, including particle size distri-bution, are estimated at the edge of the field. Plantnutrients and pesticides are simulated and storm loadand average concentrations of sediment-associatedand dissolved chemicals are determined in the runoff,sediment, and percolation through the root zone(Leonard and Knisel, 1984).
User defined management activities can be simulatedby CREAMS. These activities include aerial spraying(foliar or soil directed) or soil incorporation ofpesticides, animal waste management, andagricultural best management practices (minimumtillage, terracing, etc.).
Calibration is not specifically required for CREAMSsimulation, but is usually desirable. The modelprovides accurate representation of the various soilprocesses. Most of the CREAMS parameter values arephysically measurable. The model has the capabilityof simulating 20 pesticides at one time.
Groundwater Loading Effects of Agricultural ManagementSystems (GLEAMS) was developed by the UnitedStates Department of Agriculture?AgricultureResearch Service (Leonard et al., 1987) to utilize themanagement oriented physically based CREAMSmodel (Knisel, 1980) and incorporate a component forvertical flux of pesticides in the root zone. A nitrogencomponent for the root zone is in development.
GLEAMS consists of three major components namelyhydrology, erosion/sediment yield, and pesticides.Precipitation is partitioned between surface runoffand infiltration and water balance computations aredone on a daily basis. Surface runoff is estimatedusing the Soil Conservation Service Curve Number
Method as modified by Williams and Nicks (1982).The soil is divided into various layers, with aminimum of 3 and a maximum of 12 layers of variablethickness are used for water and pesticide routing(Knisel et al., 1989).
6. Data Needs/Availability
The data needed for CREAMS are quite detailed. AsCREAMS is a continuous simulation model the dataneeds are extensive. Meteorologic data consisting ofdaily or breakpoint precipitation is required forhydrology simulation. Monthly solar radiation and airtemperature data is also needed for estimatingcomponents of the hydrological cycle. Data regardingsoil type and properties along with information oncrops to be grown is needed. A broad range of valuesfor various model parameters can be obtained fromthe user's manual.
7. Output of the Assessment
Various output options are available for hydrologyand nutrient simulations, including storm, monthly,or annual summary. Output for each segment of theoverland flow and channel elements is available fromareas in the watershed where intense erosion ordeposition can be identified.
8. Limitations
The maximum size of the simulated area is limited toa field plots. A watershed scale version (Opus) ofCREAMS/GLEAMS is currently under development(Ferreira, personal communication). The model islimited in data management and handling. The modelcannot simulate instream processes. AlthoughCREAMS has been applied in a wide range of climaticregimes, there is concern regarding its simulationcapability for snow accumulation, melt, and resultingrunoff, and hydrologic impacts of frozen groundconditions (see Jamieson and Clausen, 1988; Kauppi,1982; Knisel et al., 1983).
9. Hardware/Software Requirements
The program is written in standard FORTRAN 77 andhas been installed on IBM PC/AT-compatibles. Ahard disk is required for operation of the programand a math co-processor is highly recommended. Theprogram can be obtained on floppy disk for MS/DOSoperating systems.
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10. Experience
CREAMS has been extensively applied in a widevariety of hydrologic settings with good success.CREAMS has been used for in a wide variety ofhydrologic and water quality studies (Smith andWilliams, 1980; Morgan and Morgan, 1982; Lane andFerreira, 1980; Kauppi, 1982; Knisel et al., 1983; andJamieson and Clausen, 1988). Crowder et al. (1985)have used CREAMS in conjunction with an economicmodel to evaluate effects of conservation practices.
11. Validation/Review
The model has been validated by the developersalong with independent experts.Erosion/sedimentation component of the CREAMSmodel has been verified by Foster and Ferreira (1981).Smith and Williams (1980) have validated thehydrology submodel at 46 sites in the southern andmidwestern portions of United States. GLEAMSmodel has been validated with field data forFenamiphos and its metabolites by Leonard et al.,1990. The authors report satisfactory comparisonbetween observed and simulated results.
12. Contact
To obtain copies of the model please write or call Dr.Walt Knisel or Frank Davis at the following address:
USDA-Agricultural Research ServiceSoutheast Watershed Research Lab
P.O. Box 946Tifton, Georgia 31793
(912) 386-3462
13. References
Beasley, D.B. and D.L. Thomas. 1989. Application ofWater Quality Models for Agricultural andForested Watersheds. Southern Cooperative SeriesBulletin No. 338. Agricultural Experiment Station,University of Georgia, Athens, GA.
Crowder, B.M., H.B. Pionke, D.J. Epp and C.E. Young.1985. ?Using CREAMS and Economic Modeling toEvaluate Conservation Practices: An Application.?Journal of Environmental Quality, 14(3):428-434.
Foster, G.R. and V.A. Ferreira. 1981. ?Deposition in
Uniform Grade Terrace Channels.? In: CropProduction with Conservation in the 80's. AmericanSociety of Agricultural Engineers, St. Joseph,Michigan. pp. 185-197.
Jamieson, C.A. and J.C. Clausen. 1988. Tests of theCREAMS Model on Agricultural Fields in Vermont.Water Resources Bulletin, 24(6):1219-1226.
Kauppi, L. 1982. ?Testing the Application of CREAMSto Finnish Conditions.? In: European and UnitedStates Case Studies in Application of the CREAMSModel. V. Svetlosanov and W.G. Knisel (editors).International Institute for Applied SystemsAnalysis. Laxenberg, Austria. pp. 43-47.
Knisel, W. (ed). 1980. CREAMS: A Field Scale Modelfor Chemicals, Runoff, and Erosion fromAgricultural Management Systems. U.S.Department of Agriculture. Conservation ResearchReport No. 26. 640 pp.
Knisel, W.G., G.R. Foster and R.A. Leonard. 1983.?CREAMS: A System for Evaluating ManagementPractices.? In: Agricultural Management and WaterQuality. F.W. Schaller and G.W. Bailey (editors).Iowa State University Press, Ames, Iowa, pp. 178-199.
Knisel, W.G., R.A. Leonard and F.M. Davis. 1989.?Agricultural Management Alternatives: GLEAMSModel Simulations.? Proceedings of the ComputerSimulation Conference. Austin, Texas, July 24-27,pp. 701-706.
Lane, L.J. and V.A. Ferreira. 1980. ?SensitivityAnalysis.? In: CREAMS: A Field-Scale Model forChemicals, Runoff, and Erosion from AgriculturalManagement Systems. W.G. Knisel (editor). U.S.Department of Agriculture, Conservation ReportNo. 26, U.S. Government Printing Office,Washington, D.C., pp. 113-158.
Leonard, R.A., W.G. Knisel and D.A. Still. 1987.?GLEAMS: Groundwater Loading Effects ofAgricultural Management Systems.? Trans. of theASAE, 30(5):1403-1418.
Leonard, R.A. and V.A. Ferreira. 1984. CREAMS2?TheNutrient and Pesticide Models. Proc. NaturalResources Modeling Symposium. AgriculturalResearch Service. U.S. Department of Agriculture.
Leonard, R.A. and W.G. Knisel. 1984. Model Selection
83
for Nonpoint Source Pollution and ResourceConservation. In: Proc. of the InternationalConference on Agriculture and Environment 1984.Venice, Italy. pp. E1-E18.
Leonard, R.A., W.G. Knisel, F.M. Davis and A.W.Johnson. 1990. ?Validating GLEAMS with FieldData for Fenamiphos and its Metabolites.? Journalof Irrigation and Drainage Engineering, 116(1):24-35.
Morgan, R.P.C. and D.D.V. Morgan. 1982. ?PredictingHillslope Runoff and Erosion in the UnitedKingdom: Preliminary Trials with the CREAMSModel.? In: European and United States CaseStudies in Application of the CREAMS Model. V.Svetlosanov and W.G. Knisel (editors).International Institute for Applied SystemsAnalysis. Laxenburg, Austria. pp. 83-97.
Smith, R.E. and J.R. Williams. 1980. ?Simulation of theSurface Water Hydrology.? In: CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosionfrom Agricultural Management Systems. W.G.Knisel (editor). U.S. Department of Agriculture,Conservation Report No. 26, U.S. GovernmentPrinting Office, Washington, D.C., pp. 13-35.
Nonpoint Source Model Review
1. Name of the Method
Hydrological Simulation Program?Fortran (HSPF)Stream Transport and Agricultural Runoff of Pesticides for Exposure Assessment (STREAM)
2. Type of Method
Surface Water Model: Simple Approachxxx Surface Water Model: Refined Approach
Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach
xxx Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff watersxxx surface watersxxx ground waters
84
Source/Release Types:
xxx Continuous xxx Intermittentxxx Single xxx Multiple xxx Diffuse
Level of Application:
xxx Screening xxx Intermediate xxx Detailed
Type of Chemicals:
xxx Conventional xxx Organic Metals
Unique Features:
xxx Addresses degradation productsxxx Integral Database/Database manager
Integral Uncertainty Analysis Capabilitiesxxx Interactive Input/Execution Manager
4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
85
5. Description of the Method/Techniques
Hydrological Simulation Program?FORTRAN (HSPF) isa comprehensive package for simulation of watershedhydrology and water quality for both conventionaland toxic organic pollutants. HSPF incorporates thewatershed scale ARM and NPS models into a basic-scale analysis framework that includes fate andtransport in one-dimensional stream channels. It is theonly comprehensive model of watershed hydrologyand water quality that allows the integrated sim-ulation of land and soil contaminant runoff processeswith instream hydraulic and sediment-chemicalinteractions.
The result of this simulation is a time history of therunoff flow rate, sediment load, and nutrient andpesticide concentrations, along with a time history ofwater quantity and quality at any point in awatershed. HSPF simulates three sediment types(sand, silt, and clay) in addition to a single organicchemical and transformation products of that chemi-cal. The transfer and reaction processes included arehydrolysis, oxidation, photolysis, biodegradation, avolatilization, and sorption. Sorption is modeled as afirst-order kinetic process in which the user mustspecify a desorption rate and an equilibrium partitioncoefficient for each of the three solid types.Resuspension and settling of silts and clays (cohesivesolids) are defined in terms of shear stress at thesediment-water interface. For sands, the capacity ofthe system to transport sand at a particular flow iscalculated and resuspension or settling is defined bythe difference between the sand in suspension and thecapacity. Calibration of the model requires data foreach of the three solids types. Benthic exchange ismodeled as sorption/desorption and desorp-tion/scour with surficial benthic sediments. Underly-ing sediment and pore water are not modeled.
6. Data Needs/Availability
Data needs for HSPF are extensive. HSPF is acontinuous simulation program and requirescontinuous data to drive the simulations. As aminimum, continuous rainfall records are required todrive the runoff model and additional records ofevapotranspiration, temperature, and solar intensityare desirable. A large number of model parameterscan also be specified although default values areprovided where reasonable values are available. HSPFis a general-purpose program and special attentionhas been paid to cases where input parameters are
omitted. Option flags allow bypassing of wholesections of the program where data are not available.
7. Output of the Assessment
HSPF produces a time history of the runoff flow rate,sediment load, and nutrient and pesticideconcentrations, along with a time history of waterquantity and quality at any point in a watershed.Simulation results can be processed through afrequency and duration analysis routine that producesoutput compatible with conventional toxicologicalmeasures (e.g., 96-hour LC50).
8. Limitations
HSPF assumes that the ?Stanford Watershed Model?hydrologic model is appropriate for the area beingmodeled. Further, the instream model assumes thereceiving water body model is well-mixed with widthand depth and is thus limited to well-mixed riversand reservoirs. Application of this methodologygenerally requires a team effort because of itscomprehensive nature.
9. Hardware/Software Requirements
The program is written in standard FORTRAN 77 andhas been installed on systems as small as IBMcompatibles (80386/486). A hard disk is required foroperation of the program and a math co-processor isrequired. No special peripherals other than a printerare required. The program is maintained for both theIBM PC-compatible and the DEC/VAX with VMSoperating system. Executable code prepared with theLahey FORTRAN compiler and Phar Lap DOSextender is available for the MS/DOS environment.Source code only is available for the VAXenvironment.
The program can be obtained in either floppy diskformat for MS/DOS operation systems, or throughthe CEAM BBS or the CEAM Internet nodeearth1.epa.gov with interactive installation program.This program has been installed on a wide range ofcomputers world-wide with no or minormodifications.
10. Experience
HSPF and the earlier models from which it wasdeveloped have been extensively applied in a wide
86
variety of hydrologic and water quality studies(Barnwell and Johanson, 1981; Barnwell and Kittle,1984) including pesticide runoff testing (Lorber andMulkey, 1981), aquatic fate and transport modeltesting (Mulkey et al., 1986; Schnoor et al., 1987)analyses of agricultural best management practices(Donigian et al., 1983a; 1983b; Imhoff et al., 1983) andas part of pesticide exposure assessments in surfacewaters (Mulkey and Donigian, 1984).
An application of HSPF to five agriculturalwatersheds in a screening methodology for pesticidereview is given in Donigian (1986). The StreamTransport and Agricultural Runoff for ExposureAssessment (STREAM) Methodology applies theHSPF program to various test watersheds for fivemajor crops in four agricultural regions in the U.S.,defines a ? representative watershed? based onregional conditions and an extrapolation of thecalibration for the test watershed, and performs asensitivity analysis on key pesticide parameters togenerate cumulative frequency distributions ofpesticide loads and concentrations in each region. Theresulting methodology requires the user to evaluateonly the crops and regions of interest, the pesticideapplication rate, and three pesticide parameters? thepartition coefficient, the soil/sediment decay rate, andthe solution decay rate.
11. Validation/Review
The program has been validated with both field dataand model experiments and has been reviewed byindependent experts. Numerous citations for modelapplications are included in the References below.Recently, model refinements for instream algorithmsrelated to pH and sediment-nutrient interactions havebeen sponsored by the USGS and the EPA ChesapeakeBay Program, respectively.
12. Contact
The model is available from the Center for ExposureAssessment Modeling at no charge. Mainframeversions of the programs compatible with the DECVAX systems are available on standard on-half inch,9-track magnetic tape. When ordering tapes, pleasespecify the type of computer system that the modelwill be installed on (VAX, PRIME, HP, Cyber, IBM,etc.), whether the tape should be non-labeled, if non-labeled specify the storage formate (EBCDIC orASCII), or if the tape should be formatted as a VAXfiles-11, labeled (ASCII) tape for DEC systems. Model
distributions tapes contain documentation coveringinstallation instructions on DEC systems, FORTRANsource code files, and test input data sets and outputfiles that may be used to test and confirm theinstallation of the model on your system. Users areresponsible for installing programs.
Requests for PC versions of the models should beaccompanied by 6 formatted double-sided, high-density (DS/HD), error free diskettes. Please do notsend 5.25" diskettes. Model distribution diskettescontain documentation covering installationinstructions on PC systems, DOS batch files forcompiling, linking, and executing the model,executable task image(s) ready for execution of themodel(s), all associated runtime files, and test inputdata sets and corresponding output files that may beused to test and confirm the installation of the modelon your PC or compatible system.
To obtain copies of the models, please sendappropriate number of formatted diskettes to theattention of Model Distribution Coordinator at thefollowing address:
Center for Exposure Assessment ModelingU.S. Environmental Protection Agency
Athens Environmental Research LaboratoryAthens, Georgia 30605
(706) 546-3549USA
Program and/or user documentation, or instructionson how to order documentation, will accompany eachresponse.
13. References
Barnwell, T.O. 1980. An Overview of the HydrologicSimulation Program?FORTRAN, a SimulationModel for Chemical Transport and Aquatic RiskAssessment. In: Aquatic Toxicology and HazardAssessment: Proceedings of the Fifth AnnualSymposium on Aquatic Toxicology, ASTM SpecialTech. Pub. 766, ASTM, 1916 Race Street,Philadelphia, PA 19103.
Barnwell, T.O. and R. Johanson. 1981. HSPF: AComprehensive Package for Simulation ofWatershed Hydrology and Water Quality. In:Nonpoint Pollution Control: Tools and Techniques forthe Future. Interstate Commission on the PotomacRiver Basin, 1055 First Street, Rockville, MD 20850.
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Barnwell, T.O. and J.L. Kittle. 1984. ?HydrologicSimulation Program?FORTRAN: Development,Maintenance and Applications.? In: ProceedingsThird International Conference on Urban StormDrainage. Chalmers Institute of Technology,Goteborg, Sweden.
Bicknell, B.R., A.S. Donigian Jr. and T.O. Barnwell.1984. Modeling Water Quality and the Effects ofBest Management Practices in the Iowa RiverBasin. J. Wat. Sci. Tech., 17:1141-1153.
Chew, Y.C., L.W. Moore, and R.H. Smith. 1991.?Hydrologic SImulation of Tennessee's NorthReelfoot Creek watershed? J. Water PollutionControl Federation 63(1):10-16.
Donigian, A.S., Jr., J.C. Imhoff and B.R. Bicknell. 1983.Modeling Water Quality and the Effects of BestManagement Practices in Four Mile Creek, Iowa.EPA Contract No. 68-03-2895, EnvironmentalResearch Laboratory, U.S. EPA, Athens, GA. 30613.
Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell and J.L.Kittle, Jr. 1984. Application Guide for theHydrological Simulation Program?FORTRAN EPA600/3-84-066, Environmental Research Laboratory,U.S. EPA, Athens, GA. 30613.
Donigian, A.S., Jr., D.W. Meier and P.P. Jowise. 1986.Stream Transport and Agricultural Runoff forExposure Assessment: A Methodology.EPA/600/3-86-011, Environmental ResearchLaboratory, U.S. EPA, Athens, GA. 30613.
Donigian, A.S., Jr., B.R. Bicknell, L.C. Linker, J.Hannawald, C. Chang, and R. Reynolds. 1990.Chesapeake Bay Program Watershed ModelApplication to Calculate Bay Nutrient Loadings:Preliminary Phase I Findings andRecommendations. Prepared by AQUA TERRAConsultants for U.S. EPA Chesapeake Bay Program,Annapolis, MD.
Hicks, C.N., W.C. Huber and J.P. Heaney. 1985.Simulation of Possible Effects of Deep Pumping onSurface Hydrology Using HSPF. In: Proceedings ofStormwater and Water Quality Model User GroupMeeting. January 31?February 1, 1985. T.O.Barnwell, Jr., ed. EPA-600/9-85/016. Environ-mental Research Laboratory, Athens, GA.
Johanson, R.C., J.C. Imhoff, J.L. Kittle, Jr. and A.S.
Donigian. 1984. Hydrological SimulationProgram?FORTRAN (HSPF): Users Manual forRelease 8.0, EPA-600/3-84-066, EnvironmentalResearch Laboratory, U.S. EPA, Athens, GA. 30613.
Johanson, R.C. 1989. Application of the HSPF Modelto Water Management in Africa. In: Proceedings ofStormwater and Water Quality Model Users GroupMeeting. October 3-4, 1988. Guo, et al., eds. EPA-600/9-89/001. Environmental Research Laboratory,Athens, GA.
Lorber, M.N. and L.A. Mulkey. 1982. An Evaluation ofThree Pesticide Runoff Loading Models. J. Environ.Qual. 11:519-529.
Moore, L.W., H. Matheny, T. Tyree, D. Sabatini andS.J. Klaine. 1988. ?Agricultural Runoff Modeling ina Small West Tennessee Watershed,? J. WaterPollution Control Federation 60(2):242-249.
Motta, D.J. and M.S. Cheng. 1987. ?The Henson CreekWatershed Study? In: Proceedings of Stormwaterand Water Quality Users Group Meeting. October15-16, 1987. H.C. Torno, ed. Charles Howard andAssoc., Victoria, BC, Canada.
Mulkey, L.A., R.B. Ambrose, and T.O. Barnwell. 1986.?Aquatic Fate and Transport Modeling Techniquesfor Predicting Environmental Exposure to OrganicPesticides and Other Toxicants?A ComparativeStudy.? In: Urban Runoff Pollution, Springer-Verlag,New York, NY.
Nichols, J.C. and M.P. Timpe. 1985. Use of HSPF tosimulate Dynamics of Phosphorus in FloodplainWetlands over a Wide Range of HydrologicRegimes. In: Proceedings of Stormwater and WaterQuality Model Users Group Meeting, January31?February 1, 1985. T.O. Barnwell, Jr., ed. EPA-600/9-85/016, Environmental Research Laboratory,Athens, GA.
Schnoor, J.L., C. Sato, D. McKetchnie, and D. Sahoo.1987. Processes, Coefficients, and Models forSimulating Toxic Organics and Heavy Metals inSurface Waters. EPA/600/3-87/015. U.S. Environ-mental Protection Agency, Athens, GA. 30613.
Schueler, T.R. 1983. Seneca Creek WatershedManagement Study, Final Report, Volumes I and II.Metropolitan Washington Council of Governments,Washington, DC.
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Song, J.A., G.F. Rawl, W.R. Howard. 1983. LakeManatee Watershed Water Resources Evaluationusing Hydrologic Simulation Program?FORTRAN(HSPF) In: Colloque sur la Modelisation des EauxPluviales. Septembre 8-9, 1983. P. Beron, et al., T.Barnwell, editeurs. GREMU?83/03 EcolePolytechnique de Montreal, Quebec, Canada.
Sullivan, M.P. and T.R. Schueler. 1982. The PiscatawayCreek Watershed Model: A Stormwater andNonpoint Source Management Tool. In:Proceedings Stormwater and Water QualityManagement Modeling and SWMM Users GroupMeeting, October 18-19, 1982. Paul E. Wisner, ed.Univ. of Ottawa, Dept. Civil Engr., Ottawa, Ont.,Canada.
Weatherbe, D.G. and Z. Novak. 1985. Development ofWater Management Strategy for the Humber River.In: Proceedings Conference on Stormwater andWater Quality Management Modeling, September6-7, 1984. E.M. and W. James, ed. ComputationalHydraulics Group, McMaster University,Hamilton, Ont., Canada.
Woodruff, D.A., D.R. Gaboury, R.J. Hughto and G.K.Young. 1981. Calibration of Pesticide Behavior on aGeorgia Agricultural Watershed Using HSP-F. In:Proceedings Stormwater and Water Quality ModelUsers Group Meeting, September 28-29, 1981. W.James, ed. Computational Hydraulics Group,McMaster University, Hamilton, Ont., Canada.
Udhiri, S., M-S Cheng and R.L. Powell. 1985. TheImpact of Snow Addition on Watershed AnalysisUsing HSPF. In: Proceedings of Stormwater andWater Quality Model Users Group Meeting,January 31?February 1, 1985. T.O. Barnwell, Jr., ed.EPA-600/9-85/016, Environmental ResearchLaboratory, Athens, GA.
Nonpoint Source Model Review
1. Name of the Method
Pesticide Root Zone Model?PRZM
2. Type of Method
xxx Surface Water Model: Simple Approach Surface Water Model: Refined Approach Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach
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xxx Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff waters surface waters
xxx ground waters (vadose and root zone)
Source/Release Types:
Continuous xxx Intermittent Single xxx Multiple xxx Diffuse
Level of Application:
xxx Screening xxx Intermediate xxx Detailed
Type of Chemicals:
Conventional xxx Organic Metals (pesticides)
Unique Features:
xxx Addresses degradation products Integral Database/Database manager Integral Uncertainty Analysis Capabilities Interactive Input/Execution Manager
4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
Pesticide Root Zone Model (PRZM) was developed atthe U.S. EPA Environmental Research Laboratory inAthens, Georgia by Carsel et al. (1984). It is a one-dimensional, dynamic, compartmental model that canbe used to simulate chemical movement inunsaturated zone within and immediately below theplant root zone. The model is divided into two majorcomponents namely, the hydrology (and hydraulics)and chemical transport. The hydrology componentwhich calculates runoff and erosion is based upon theSoil Conservation Service curve number procedureand the Universal Soil Loss Equation respectively.Evapotranspiration is estimated directly from panevaporation or by an empirical formula if panevaporation data is not available. Soil-water capacityterms including field capacity, wilting point, andsaturation water content are used for simulatingwater movement within the unsaturated zone.Irrigation application is also within modelcapabilities.
Pesticide application on soil or on the plant foliage areconsidered in the chemical transport simulation.Dissolved, adsorbed, and vapor-phase concentrationsin the soil are estimated by simultaneouslyconsidering the processes of pesticide uptake byplants, surface runoff, erosion, decay, volatilization,foliar washoff, advection, dispersion, and retardation.The user has two options to solve the transportequations using the original backward differenceimplicit scheme or the method of characteristics(Dean et al., 1989). As the model is dynamic it allowsconsiderations of pulse loads.
PRZM is an integral part of a unsaturated/saturatedzone model RUSTIC (Dean et al., 1989). RUSTIC (Riskof Unsaturated/Saturated Transport andTransformation of Chemical Concentrations) linksthree subordinate models in order to predict pesticidefate and transport through the crop root zone, andsaturated zone to drinking water wells throughPRZM, VADOFT, SAFTMOD.
VADOFT is a one-dimensional finite element modelwhich solves Richard's equation for water flow in theunsaturated zone. VADOFT can also simulate the fateand transport of two parent and two daughterproducts. SAFTMOD is a two-dimensional finiteelement model which simulates flow and transport inthe saturated zone in either an X-Y or X-Zconfiguration. The three codes PRZM, VADOFT, and
SAFTMOD are linked together through an executionsupervisor which allows users to build models for sitespecific situation. In order to perform exposureassessments, the code is equipped with a Monte Carlopre and post processor (Dean et al., 1989).
6. Data Needs/Availability
The meteorological data needed by the model consistof daily rainfall, potential evaporation, and airtemperature. If pesticide volatilization is to besimulated then additional meteorological dataconsisting daily wind speed and solar radiation areneeded. Soils and land use data are required whichcan be obtained from local USDA-SCS field offices.The data regarding pesticide input parameters can beobtained from the user's manual or from publishedresearch.
7. Output of the Assessment
Predictions are made on a daily basis. Output can alsobe summarized for a daily, monthly, or annual period.Daily time series values of various fluxes and soilstorages can be written to sequential files for furtherevaluation. In addition, a Special Action' optionallows the user to output soil profile pesticideconcentrations at user specified times (Dean et al.,1989).
8. Limitations
One of the model limitation is that it one-dimensionalin the vertical direction and hence does not handlelateral flow. PRZM only simulates downwardmovement of water and does not account for diffusivemovement due to soil water gradients. This processhas been identified to be important when simulatingthe effects of volatilization by Jury et al. (1984). Themodel only simulates organic chemicals, for examplepesticides.
9. Hardware/Software Requirements
The program is written in standard FORTRAN 77 andhas been installed on IBM PC/AT-compatibles. Ahard disk is required for operation of the programand a math co-processor is required. The program canbe obtained on floppy disk for MS/DOS operatingsystems.
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10. Experience
PRZM has been used to study Aldicarb application tocitrus in Florida (Jones et al., 1983), and potatoes inNew York (Carsel et al., 1985) and Wisconsin (Jones,1983). It has also been used for Metalaxyl applicationto tobacco in Florida and Maryland (Carsel et al.,1986) and to Atrazine and chloride application to cornin Georgia (Carsel et al., 1985).
11. Validation/Review
The PRZM model has undergone testing with fielddata in New York and Wisconsin (potatoes), Florida(citrus), and Georgia (corn) (Carsel, et al., 1985, Jones1983, Jones et al., 1983). The results of these testsdemonstrate that PRZM is a useful tool for evaluatinggroundwater threats for pesticide use.
12. Contact
To obtain copies of the user's manual and thecomputer program contact
Center for Exposure Assessment ModelingU.S. Environmental Protection Agency
Environmental Research LaboratoryAthens, GA. 30605
(706) 546-3549
13. References
Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean andP. Jowise. 1984. User's Manual for the PesticideRoot Zone Model (PRZM): Release 1. EPA-600/3-84-109. U.S. Environmental Protection Agency.Environmental Research Laboratory, Athens, GA.
Carsel, R.F., L.A. Mulkey, M.N. Lorber and L.B.Baskin. 1985. ?The Pesticide Root Zone Model(PRZM): A Procedure for Evaluating PesticideLeaching Threats to Ground Water? EcologicalModeling 30:49-69.
Carsel, R.F., W.B. Nixon and L.G. Balentine. 1986.?Comparison of Pesticide Root Zone ModelPredictions with Observed Concentrations for theTobacco Pesticide Metalaxyl in Unsaturated ZoneSoils? Environ. Toxicol. Chem. 5:345-353.
Dean, J.D., P.S. Huyakorn, A.S. Donigian, Jr., K.A.Voos, R.W. Schanz, Y.J. Meeks and R.F. Carsel. 1989.
Risk of Unsaturated/Saturated Transport andTransformation of Chemical Concentrations(RUSTIC). EPA/600/3-89/048a. U.S. Environ-mental Protection Agency. Environmental ResearchLaboratory, Athens, GA.
Jones, R.L. 1983. ?Movement and Degradation ofAldicarb Residues in Soil and Ground Water.?Presented at the CETAC Conference onMultidisciplinary Approaches to EnvironmentalProblems, November 6-9, Crystal City, VA.
Jones, R.L., P.S.C. Rao and A.G. Hornsby. 1983. ?Fateof Aldicarb in Florida Citrus Soil 2, ModelEvaluation.? Presented at the Conference onCharacterization and Monitoring of Vadose(Unsaturated) Zone, December 8-10, Las Vegas,NV.
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Nonpoint Source Model Review
1. Name of the Method
Simulator for Water Resources in Rural Basins? (SWRRB)Pesticide Runoff Simulator? (PRS)
2. Type of Method
Surface Water Model: Simple Approachxxx Surface Water Model: Refined Approach
Air Model: Simple Approach Air Model: Refined Approach Soil (Groundwater) Model: Simple Approach
xxx Soil (Groundwater) Model: Refined Approach Multi-media Model: Simple Approach Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff waters surface waters
xxx ground waters
Source/Release Types:
Continuous Intermittent Single Multiple xxx Diffuse
Level of Application:
xxx Screening xxx Intermediate xxx Detailed
Type of Chemicals:
xxx Conventional xxx Organic Metals
Unique Features:
Addresses degradation products Integral Database/Database manager
xxx Integral Uncertainty Analysis Capabilities Interactive Input/Execution Manager
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4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
Pesticide Runoff Simulator (PRS) was developed for theU.S. EPA Office of Pesticide and Toxic Substances byComputer Sciences Corporation (1980) to simulatepesticide runoff and adsorption into the soil on smallagricultural watersheds. PRS is based on SWRRB(Simulator for Water Resources in Rural Basins)originally developed by Williams et al. (1985) at theUSDA.
The PRS hydrology and sediment simulation is basedon the USDA CREAMS model. The SCS curvenumber technique is used to predict surface runoff.Sediment yield is simulated using a modified versionof the Universal Soil Loss Equation and a sedimentrouting model.
The pesticide component of PRS is a modified versionof the CREAMS pesticide model. Pesticide application(foliar and soil applied) can be removed byatmospheric loss, wash off by rainfall, and leachinginto the soil. Pesticide yield is divided into a solublefraction and an adsorbed phase based on anenrichment ratio.
The model includes a built in weather generator basedon temperature, solar radiation, and precipitationstatistics. Calibration is not specifically required, butis usually desirable.
Simulator for Water Resources in Rural Basins (SWRRB)was developed by Williams et al. (1985), and Arnoldet al., (1989) for evaluating basin scale water quality.SWRRB operates on a daily time step and simulatesweather, hydrology, crop growth, sedimentation, andnitrogen, phosphorous, and pesticide movement. Themodel was developed by modifying the CREAMS(Knisel, 1980) daily rainfall hydrology model forapplication to large, complex, rural basins.
Surface runoff is calculated using the SoilConservation Service Curve Number technique.Sediment yield is computed for each basin by usingthe Modified Universal Soil Loss Equation (Williamsand Berndt, 1977). The channel and floodplainsediment routing model is composed of twocomponents operating simultaneously (depositionand degradation). Degradation is based on Bagnold'sstream power concept and deposition is based on thefall velocity of the sediment particles (Arnold et al.,1989).
Return flow is calculated as a function of soil watercontent and return flow travel time. The percolationcomponent uses a storage routing model combinedwith a crack flow model to predict the flow throughthe root zone. The crop growth model (Arnold et al.,1989) computes total biomass each day during thegrowing season as a function of solar radiation andleaf area index.
The pollutant transport portion is subdivided into onepart handling soluble pollutants and another parthandling sediment attached pollutants. The methodsused to predict nitrogen and phosphorus yields fromthe rural basins are adopted from CREAMS (Knisel,1980). The nitrogen and phosphorus calculations areperformed using relationships between chemicalconcentration, sediment yield and runoff volume.
The pesticide component is directly taken from Holstand Kutney (1989) and is a modification of theCREAMS (Smith and Williams, 1980) pesticide model.The amount of pesticide reaching the ground or plantsis based on a pesticide application efficiency factor.Empirical equations are used for calculating pesticidewashoff which are based on threshold rainfallamount. Pesticide decay from the plants and the soilare predicted using exponential functions based onthe decay constant for pesticide in the soil, and halflife of pesticide on foliar residue.
6. Data Needs/Availability
Meteorologic data comprising of daily precipitationand solar radiation are required for hydrologysimulations. Another set of input data consists ofsoils, land use, fertilizer, and pesticide application.The soils and land use data can be obtained fromUSDA-SCS soil survey maps. Some guidance isavailable in the manual for estimating parametersrequired for nutrient and pesticide simulation.
7. Output of the Assessment
The model predicts daily runoff volume and peakrate, sediment yield, evapotranspiration, percolation,return flow, and pesticide concentration in runoff andsediment.
8. Limitations
There is very minimal model documentation. In thehydrology component the snow accumulation
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processes are ignored, and for the case of pesticidesno comprehensive instream simulation is available.Nutrient transformations along with pesticidedaughter products are not accounted for in the model.
9. Hardware/Software Requirements
The PRS model is operational on the EPA NationalComputer Center on an IBM 370/168 computer underMVS. The model may be accessed via WYLBUR formodification of the source code, creation ormodification of input datasets, and submission ofbatch executions of the model.
The SWRRB program is written in standardFORTRAN 77 and has been installed on IBM PC/ATand compatibles. A hard disk is required for operationof the program and a math co-processor is highlyrecommended.
10. Experience
The SWRRB model has been used by the ExposureAssessment Branch, Hazard Evaluation Division, andthe Office of Pesticide Programs of the USEPA(Arnold et al., 1989).
11. Validation/Review
SWRRB was tested on 11 large watersheds by Arnoldand Williams (1987). These watersheds were locatedat eight Agricultural Research Service locationsthroughout the United States. The results showed thatSWRRB can realistically simulate water and sedimentyield under a wide range of soils, climate, land-use,topography, and management systems.
12. Contact
For copies of the SWRRB program and the usermanual contact
Nancy Sammons808 East Blackland Road
Temple, Texas 76502(817) 770-6512
13. References
Arnold, J.G., J.R. Williams, A.D. Nicks and N.B.Sammons. 1989. ?SWRRB, A Basin Scale Simulation
Model for Soil and Water Resources Management.?Texas A&M Press. 255 pp. (In Press).
Arnold, J.G. and J.R. Williams. 1987. ?Validation ofSWRRB?Simulator for Water Resources in RuralBasins.? J. of Water Resources Planning andManagement. 113(2):243-256.
Computer Science Corporation. 1980. Pesticide RunoffSimulator User's Manual. U.S. EPA, Office ofPesticides and Toxic Substances, Washington, D.C.
Holst, R.W. and L.L. Kutney. 1989. ?U.S. EPASimulator for Water Resources in Rural Basins.?Exposure Assessment Branch, Hazard EvaluationDivision, Office of Pesticide Programs, U.S. EPA(draft).
Knisel, W.G. (ed.). 1980. ?CREAMS, A Field ScaleModel for Chemicals, Runoff, and Erosion fromAgricultural Management Systems.? USDAConservation Research Report No. 26, 643 pp.
Smith, R.E. and J.R. Williams. 1980. ?Simulation of theSurface Water Hydrology.? In: CREAMS, A FieldScale Model for Chemicals, Runoff, and Erosionfrom Agricultural Management Systems. W.G.Knisel editor. USDA Conservation Research ReportNo. 26, pp. 13-35.
Williams, J.R. and H.D. Berndt. 1977. Sediment yieldprediction based on watershed hydrology.Transactions of the ASAE. 20(6):1100-1104.
Williams, J.R., A.D. Nicks and J.G. Arnold. 1985.?Simulator for Water Resources in Rural Basins.?ASCE J. Hydraulic Engineering. 111(6):970-986.
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Nonpoint Source Model Review
1. Name of the Method
Unified Transport Model for Toxic Materials?UTM-TOX
2. Type of Method
Surface Water Model: Simple Approachxxx Surface Water Model: Refined Approach
Air Model: Simple Approachxxx Air Model: Refined Approach
Soil (Groundwater) Model: Simple Approachxxx Soil (Groundwater) Model: Refined Approach
Multi-media Model: Simple Approachxxx Multi-media Model: Refined Approach
3. Purpose/Scope
Purpose: Predict concentrations of contaminants in
xxx runoff watersxxx surface waters
ground waters (vadose and root zone)
Source/Release Types:
xxx Continuous Intermittent Single xxx Multiple xxx Diffuse
Level of Application:
Screening Intermediate xxx Detailed
Type of Chemicals:
xxx Conventional xxx Organic xxx Metals
Unique Features:
Addresses degradation products Integral Database/Database manager Integral Uncertainty Analysis Capabilities Interactive Input/Execution Manager
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4. Level of Effort
System setup: xx mandays xx manweeks manmonths manyearAssessments: mandays xx manweeks xx manmonths manyear
(Estimates reflect order-of-magnitude values and depend heavily on the experience and ability of the assessor.)
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5. Description of the Method/Techniques
Unified Transport Model for Toxic Materials (UTM-TOX)was developed by Oak Ridge National Laboratory forthe U.S. EPA Office of Pesticides and ToxicSubstances, Washington, D.C. (Patterson et al., 1983).UTM-TOX is a multimedia model that combineshydrologic, atmospheric, and sediment transport inone computer code.
The model calculates rates of flux of a chemical fromrelease to the atmosphere, through deposition on awatershed, infiltration and runoff from the soil, toflow in a stream channel and associated sedimenttransport. From these calculations mass balances canbe established, chemical budgets made, andconcentrations in the environment estimated.
The atmospheric transport model (ATM) portion ofUTM-TOX is a Gaussian plume model that calculatesdispersion of pollutants emitted from point (stack),area, or line sources. ATM operates on a monthly timestep, which is longer than the hydrologic portion ofthe model and results in the use of an averagechemical deposition falling on the watershed.
The Terrestrial Ecology and Hydrology Model(TEHM) describes soil-plant water fluxes,interception, infiltration, and storm and groundwaterflow.
The hydrologic portion of the model is from theWisconsin Hydrologic Transport Model (WHTM),which is a modified version of the StanfordWatershed Model (SWM). WHTM includes all of thehydrologic processes of the SWM and also simulatessoluble chemical movement, litter and vegetationinterception of the chemical, erosion of sorbedchemical, chemical degradation in soil and litter, andsorption in top layers of the soil. Stream transportincludes transfer between three sediment components(suspended, bed, and resident bed).
6. Data Needs/Availability
The input data includes monthly wind, hourlyprecipitation, solar radiation, daily maximum andminimum temperatures, soil characteristics,topographic information, surface watercharacteristics, sediment characteristics, and thephysiochemical properties and transformation ratesassociated with the chemical.
7. Output of the Assessment
The output can be obtained in terms of plots andtables summarizing the average monthly and annualchemical concentrations in the 8 wind sectors, insaturated and unsaturated soil layers, in runoff, out ofeach reach, and in the stems, leaves, roots and fruitsof vegetation.
8. Limitations
The model ignores the interaction between chemicalsand sediment in streams. There is a large time andspatial resolution of ATM portion of the modelrelative to the hydrologic processes. The model isquite complex and requires significant user expertise.
9. Hardware/Software Requirements
UTM-TOX is large computer program and is writtenis FORTRAN IV. The program was developed for IBM370/3033 or VAX 11/780 systems.
10. Experience
An earlier version of the model has been applied byMunro et al. (1976) to evaluate the movement of lead,cadmium, zinc, copper and sulphur through CrookedCreek Watershed. This earlier version was also usedby Huff et al. (1977) to Walker Branch Watershed. Oneof the current application of the model is reported byPatterson (1986) for estimating lead transport budgetin the Crooked Creek Watershed. No currentreferences since 1986 on the application of the modelare available.
11. Validation/Review
The model components have been field validated byseveral researchers (Culkowski and Patterson, 1976;Munro et al., 1976; and Raridon, Fields, andHenderson, 1976).
12. Contact
To obtain copies of the model and the manual contact
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Dr. M.R. PattersonOak Ridge National Laboratory
Mail Stop 6243Oak Ridge, Tennessee 37831
(615) 574-5442
13. References
Culkowski, W.M. and M.R. Patterson. 1976. AComprehensive Atmospheric Transport andDiffusion Model. ORNL/NSF/EATC-17.
Huff, D.D., G.S. Henderson, C.L. Begovich, R.L.Luxmoore and J.R. Jones. 1977. The Application ofAnalytic and Mechanistic Hydrologic Models tothe Study of Walker Branch Watershed. In:Watershed Research in Eastern North America. AWorkshop to Compare Results. D.L. Corell (editor).Vol. II, pp. 741-766.
Munro, J.K., R.J. Luxmoore, C.L. Begovich, K.R.Dixon, A.P. Watson, M.R. Patterson and D.R.Jackson. 1976. Application of the Unified TransportModel to the Movement of Pb, Cd, Zn, Cu, and Sthrough the Crooked Creek Watershed.ORNL/NSF/EATC-28.
Patterson, M.R., T.J. Sworski, A.L. Sjoreen, M.G.Browman, C.C. Coutant, D.M. Hetrick, B.D.Murphy and R.J. Raridon. 1983. A User's Manualfor UTM-TOX, A Unified Transport Model. Draft.Prepared by Oak Ridge National Laboratory, OakRidge, TN, for U.S. EPA Office of Toxic Substances,Washington, DC.
Patterson, M.R. 1986. Lead Transport Budget inCrooked Creek Watershed. In: Pollutants inMultimedia Environment. Plenum Press. 1986. pp.93-118.
Raridon, R.J., D.E. Fields and G.S. Henderson. 1976.Hydrologic and Chemical Budgets on WalkerBranch Watershed?Observations and ModelingApplications. ORNL/NSF/EATC-24.